Microsoft unveils AI cybersecurity tools that outperform rivals at half the cost

Microsoft unveils AI security tools it says outperform competing platforms

Microsoft is introducing new AI tools designed to help customers continuously streamline and automate the process of identifying and reducing their exposure to security risks. The new tools come less than a week after OpenAI lost control of two of its security models when they infiltrated the servers of startup Hugging Face. The hack, Hugging Face added, involved "a swarm of tens of thousands of automated actions" that stole internal Hugging Face credentials. The OpenAI models achieved this feat by exploiting a zero-day flaw in Hugging Face's data-processing pipeline to run malicious code that escalated the models' access to the company's high-value cloud and server clusters. Microsoft's announcements on Monday made no reference to the event, which OpenAI said was "unprecedented." The company also didn't say what would prevent the new tools from similarly going rogue. To use or not to use? Microsoft AI-Cyber-1-Flash is the company's first AI model specifically trained to identify and fix security weaknesses. For now, it's designed for software vulnerability analysis. The new model is built on the company's MAI-Thinking-1 platform. Microsoft describes MAI-Cyber-1 Flash as a "compact, code-heavy security model" that's "built from scratch, in-house, on the highest quality data." It's trained on the unique perspective Microsoft has acquired from decades of vulnerability patching and security incident responses involving a wide range of its products. The company says it processes more than 1 trillion security signals each day and gains insights from 1.6 million customers. "Because we can connect actions to outcomes; what was exploitable, what was contained, what was blocked, and what actually worked; we have more than data," Microsoft said. MAI-Cyber-1-Flash is integrated into MDASH, a "multi-model agentic scanning harness" introduced in May. The harness combines 100 security-trained AI agents to discover exploitable bugs in applications. Microsoft said MDASH with MAI-Cyber-1-Flash received a 96 percent score on CyberGYM, a standard benchmark test. The rating is 12 points higher than Anthropic's Mythos and also beats Google Gemini and OpenAI GPT. The new MDASH costs half as much to use as the previous MDASH offering. The second tool Microsoft announced on Monday is named Project Perception. It too is a collection of specialized AI agents that perform red-, blue-, and green-team functions for finding vulnerabilities, investigating them to determine their risk, and taking corrective actions, respectively. Microsoft said the platform selects the models to use based on the assigned task. Considerations that go into the decision include the model's effectiveness and the end cost to the customer. Microsoft said the decisions are shaped by "ongoing research, benchmarking and evaluation across frontier and specialized models." Microsoft said Project Perception is designed to perform 90 percent of tasks for lower costs than similar platforms from competitors. That means customers can turn to the more expensive alternatives only for the remaining 10 percent of tasks. Microsoft said the new tools respond to a seismic shift in how organizations secure their networks against catastrophic hacks. "As AI accelerates the speed and scale of cyberattacks, defenders are being asked to secure increasingly complex digital environments with approaches built for a different era," the company said. "Security teams are often forced to piece together signals, context, and risk insights across vast amounts of data, making it harder to keep pace with emerging threats." With last week's OpenAI incident evoking troubling scenes straight out of the most dystopian sci-fi novels, the tools, which are currently in preview mode, deserve a healthy dose of caution that Microsoft made no mention of. They should be closely scrutinized and evaluated before being used in production. On the other hand, there are clear risks for not adopting such tools. Balancing the risks of using AI agents versus the threat of avoiding them is a work in progress with no clear answers for now.

Microsoft launches its first cybersecurity model, plus a new agentic cybersecurity system

Microsoft on Monday launched its first cybersecurity-specialized model alongside a new AI cybersecurity platform at a small event in San Francisco, taking a big swipe at major players in the space -- namely Anthropic, Google and OpenAI. The company describes MAI-Cyber-1-Flash as a model that's built "to find challenging vulnerabilities in complex codebases." The model is built to animate MDASH, Microsoft's harness dedicated to software vulnerability identification and remediation. The new security platform is dubbed Perception, and it's designed to deploy teams of agents to assist with and automate various security workflows, including identifying and remediating bugs. The platform can also integrate with MDASH. The company claims MAI-Cyber-1-Flash is significantly more powerful (and more cost-effective) than competitor models, based on its performance on an established AI cybersecurity benchmark. "We're very very excited to announce our results," said Mustafa Suleyman, the co-founder of DeepMind and current CEO of Microsoft AI. "We have MAI-1 Cyber Flash binded [sic] with GPT 5.4 inside of the MDASH harness -- which beats out Gemini, GPT 5.5 Cyber, GPT 5.6 Sol, and Mythos 5 on Cyber Gym, which is the primary benchmark that we all use. The golden benchmark." "We're shipping this into production immediately," he added. Noting that hackers are increasingly using AI in their cyberattacks, Hayete Gallot, Microsoft's vice president for security, described Perception as a way for enterprise defenders to "defend against AI with AI at the scale and speed that the attackers have." Perception uses agentic red teams, blue teams, and green teams. The red teams can provide detailed simulations of potential attacks -- providing context about potential threat actors and the likely vulnerabilities that they might exploit. Blue teams are dedicated to detecting and triaging existing bugs, while green teams take "corrective actions" against those bugs. Dave Weston, the lead engineer for Perception, described the platform as a massive efficiency upgrade for corporate defenders. "We've gone from this taking hours and hours of manual work from multiple specialized folks across the security organization -- appsec hunters, remediation engineers, you name it -- and in minutes, we have a fix for all of this. Not only do we discover the issues and prioritize them, but we have detection, posture fixing, and even a code fix." Though AI has offered new defensive capabilities to companies, its availability to cybercriminals has given rise to a dazzling array of potential threats. Microsoft's new security tools, which the company said will be available in preview on November 3, will enter an increasingly crowded field of AI cybersecurity solutions. Earlier this year, Anthropic launched Mythos, a security platform that was released to a small coterie of partner organizations through a program called Glasswing. OpenAI has also launched its own security solution in May through a program called Day Break.

Microsoft Says Its New Cybersecurity AI Beats Industry Leaders at Half the Cost - CNET

Corinne Reichert (she/her) grew up in Sydney, Australia and moved to California in 2019.... Read full bio Microsoft has a new AI cybersecurity model called MAI-Cyber-1-Flash, and when it's combined with agentic security system MDASH and OpenAI's GPT-5.4 model, it outscores Anthropic's Claude Mythos 5 by 12 points on a key benchmark. The security product is designed for "using AI to defend against AI," Microsoft says. The combination of MAI-Cyber-1-Flash with MDASH -- which launched in May -- is called Project Perception, and it enters public preview on Aug. 3, built directly into Microsoft Defender. It will slowly roll out to all Microsoft Security products. According to benchmarks posted by Microsoft on Monday, the combination scored 96% on CyberGym, compared with Mythos 5 at 84%. Pricing is consumption-based, measured by the number of security compute units you use. As AI agents run scenarios, they consume SCUs, so the more work performed, the more you pay -- but Microsoft says cost savings are almost 50% of the current MDASH configuration on the market now. Speaking at a Microsoft briefing on Monday morning, Mustafa Suleyman, CEO of Microsoft AI, explained the handover process between MAI-Cyber-1-Flash and GPT-5.4. "MAI-Cyber-1-Flash handles about 90% of the queries. It detects the vulnerabilities, it patches them, ships them and then proves that it was actually a valid and correct solve. And then it basically defers about 10% of the queries to GPT-5.4, which is obviously a larger model, about 10x larger, and it solves those," Suleyman said. "In conjunction, as the models hand off between each other, they're actually not just able to deliver better performance than all of the other models combined -- they do so at 50% of the cost." Suleyman called the CyberGym benchmark result "quite a remarkable result." It follows the launch of Anthropic's Claude Fable 5 last month, the first publicly available model from the Mythos family. At the time, Anthropic said Mythos was so good at finding cybersecurity flaws that it could break the internet if used unchecked -- and Anthropic was forced to walk back the Fable 5 and Mythos 5 launches within days because the US government said it was aware of a way to "jailbreak" the model and bypass limits. When Mythos was first announced, it was released only to select government agencies and tech professionals. "Microsoft has long been the trusted steward of some of the most valuable, important government and enterprise data in the world over many decades. We've accrued a phenomenal amount of data in that time," Suleyman said Monday. "It's that data combined with the expertise that we have from the world-class cybersecurity experts in the company that we've been able to really drive this combined model."

Microsoft's Project Perception Announcement And How To Implement It Right

Today, Microsoft announced Project Perception, a series of red, blue, and green team agents designed to be coordinated together in an agentic architecture to evaluate infrastructure and close gaps as close to autonomously as possible. The red team agents find potential paths to compromise. The blue team agents prioritize and evaluate them. The green team agents build and implement security controls to harden the environment. This is a different level of coordination and evaluation than we have seen with previous Microsoft announcements like MDASH. While MDASH is focused on vulnerability scanning and identification, Project Perception proposes to address the entire security lifecycle, from identifying attack paths to prioritizing and implementing fixes that harden the environment and introduce new detections. Microsoft's initial demos focus on hardening web apps exclusively. They show that the blue team agents are able to pull in and evaluate threat intelligence information that can then be passed off to red team agents to evaluate attack paths, perform reconnaissance, and scan for vulnerabilities. Blue team agents can investigate and prioritize the potential attacks, and build net new detections to identify attacker behavior in the future. Green team agents can build potential fixes for vulnerabilities, and even connect to GitHub to propose the fix and open a pull request. Project Perception also includes an MCP server so the actions can also be executed in the CLI. For now, the agents are available exclusively for Microsoft Defender. As of next week, they will be available - for now - to a select group of customers in private preview. Pricing for these agents will be based on consumption. Despite pricing being separate, getting value from these agents requires the Microsoft stack. In its announcement, Microsoft states its graph is a differentiator in its approach, as it makes it simpler for agents to gather context. While this be true, it also highlights an important trend: the cyber platform push is undeniably intertwined with the AI agents and agentic systems being deployed. The more comprehensive and cohesive the visibility from the cyber platform, the better outcomes the agentic systems can have. Project Perception gives agentic coordination to users - now comes the hard work of implementation Especially juxtaposed against recent developments like OpenAI's model attack on Hugging Face, Microsoft's announcement represents a major development in a harness and capability that will soon be available to the public that promises to provide autonomous hardening of the environment that goes beyond singular agents fulfilling specific functions. Other vendors like Wiz have released red, blue, and green team agents, and ways for users to coordinate them. The differentiating element of this announcement is the coordination enabled by the agentic architecture between these agents, and the way they work together to close the loop between identification, evaluation, and remediation. Microsoft AI-Cyber-1-Flash is also here Microsoft also announced its first cybersecurity model focused on vulnerability analysis: Microsoft AI-Cyber-1-Flash. Microsoft has released several custom models over the past year, starting with the image model MAI-Image-1, with other models for transcription, reasoning, coding, and speech. However, this is its first custom cybersecurity model. The shift in custom model development by Microsoft shows their desire to control the entire security stack, which it highlights in the announcement, along with a need to manage costs and control tuning of the models for specific tasks for quality. Project Perception does not exclusively rely on Microsoft's model -- the harness has an orchestration layer to choose the best model to manage quality, reliability, latency, and cost. It's the equivalent of a multiplexer, but for models. This is a best practice that Forrester recommends to any organization building a harness or evaluating a vendor's AI capabilities -- they should all support multiple models and orchestrate between them depending on the use case. Demos and blogs can only show us so much, especially when it comes to AI. Many AI features released over the past several years talk a big game, but have constraints (like lacking business context or being too costly) that can only be understood in real-world, production deployment. Agents are non-deterministic, and will potentially take different execution paths and produce different responses. This compounds in an agentic architecture, where agents can suffer cascading failures. To make the most out of a real-world deployment of an agentic architecture: * Require observability by default. Failures are as opaque as the data that is provided, so require observability data by default to monitor, detect, and understand when something goes wrong. * Ensure least privilege access/least agency. No one wants a repeat OpenAI/Hugging Face incident. Strictly limit permissions so that agents can only access exactly what they need to at the time they need and nothing more. It's important to separate permissions at an agent level, even in an agentic architecture, to ensure agent traceability and encapsulation to specific tasks and not take irreversible actions. * Give the system access to the right data. Context is everything for an AI agent, and this is doubly true for an agentic system. Give the agent data about the enterprise environment in a properly formatted, clean structure, otherwise the agent will spend time and money on finding the right data (or potentially hallucinating and falsifying it) instead of solving the right problem. Forrester has received positive feedback from users using AI agents from Microsoft, like the Phishing Triage Agent, but this system is much more complex. Time and practitioner experience will tell. Connect With Me If you have questions related to this announcement and are a Forrester client, connect with me in an inquiry or guidance session.

Microsoft's solution to AI security: more AI and more acronyms

AI agents can break through security, but they are also the solution to defending against an increasingly dangerous ecosystem of threats. On Monday, Microsoft announced a new security model that it says helped outperform several rival AI systems on a vulnerability benchmark while cutting costs by about half. Unsurprisingly, at Redmond's security event on Monday, execs touted the tech giant's AI security prowess and introduced a new agentic security system called Project Perception, and also unveiled its first security-specialized model, MAI-Cyber-1-Flash, designed for software vulnerability analysis. Microsoft packed MAI-Cyber-1-Flash, based on Microsoft AI (MAI)'s internally developed MAI-Thinking-1 reasoning model, inside its MDASH bug-hunting harness. Its execs claim the duo - with a GPT-5.4 boost - outperforms Anthropic's bug-hunting machine Mythos and OpenAI's powerful standalone models, and costs about half the price of other leading commercial models. CyberGym's benchmarking found that MAI-Cyber-1-Flash, combined with GPT-5.4, both stuffed inside the MDASH harness, achieved a 95.95 percent success rate. For comparison, OpenAI's GPT-5.5 Cyber scored 85.6 percent and its GPT-5.6 Sol scored 83.6 percent, while Anthropic's Mythos 5 successfully handled real-world vulnerabilities 83.8 percent of the time. Google's Gemini 3.5 Flash Cyber in CodeMender achieved an 83.2 percent success rate. "This is really quite a remarkable result," Mustafa Suleyman, CEO of Microsoft AI, said during the Monday event. Within MDASH, MAI-Cyber-1-Flash handles up to 90 percent of all queries, detecting and patching the vulnerabilities while also confirming the fixes worked, and hands the remaining 10 percent of tasks off to the larger GPT-5.4, Suleyman explained. "GPT 5.4, which is obviously a larger model, about 10X larger, solves those [queries]," he said. "As the models hand off between each other, they are not just able to deliver better performance than all of the other models combined, they do so at 50 percent of the cost." In addition to the multi-model bug hunting system, Microsoft announced Project Perception, which coordinates three types of agents: red team agents that find and simulate attack paths, blue team agents that investigate and determine risk, and green team agents that remediate the issues. "We need to make sure that the defenders can defend at the scale and the speed of the attackers," Hayete Gallot, executive vice president of Microsoft Security, said. "You need a new cyber stack. So we built it. This is what we call Perception." Aside from the new security products, Redmond introduced a new AI security research arm called Microsoft Security FORGE (Frontier Offensive Research and Generative Exploration) Labs, led by Microsoft VP of Security Research Taesoo Kim, and an AI red team alliance. The latter, called the External Red Team Alliance (EXTRA), aims to expand AI safety research through a two-part initiative. First, Redmond's own AI red team provided "unrestricted gifts " to 18 university labs across six continents to support AI safety research, Microsoft data cowboy and AI red team lead Ram Shankar Siva Kumar said in a blog. "The funding is unrestricted because the objective is not to direct research outcomes toward product requirements or predefined deliverables," he wrote. "Some universities are examining the cybersecurity implications of AI systems themselves - including how models can be attacked, manipulated, or abused in operational environments. Other labs are exploring the inverse problem: how AI systems can assist defenders and improve cyber operations." The second EXTRA component will build a distributed network of specialists to participate in red teaming across very specific areas. "That includes researchers, practitioners, and regional experts who understand specific attack classes, languages, cultural contexts, or technical domains that internal teams may not fully cover alone," he added. ®

Microsoft Says New Cybersecurity AI Model Helps MDASH Hit 95.95% at Half the Cost

Microsoft has launched its first cybersecurity-specific model inside MDASH, its multi-model vulnerability identification and remediation harness. The company says MDASH, using MAI-Cyber-1-Flash and GPT-5.4, scored 95.95% on CyberGym. It also claims the configuration costs 50% less than its current best MDASH combination of GPT-5.4, GPT-5.4 mini, and GPT-5.3 Codex. Access is limited to approved MDASH customers through an Azure AI Foundry private preview. MAI-Cyber-1-Flash is designed to handle up to 90% of MDASH tasks, with GPT-5.4 reserved for the hardest 10%. It is available only inside MDASH, not as a standalone public model or general-purpose application programming interface. The headline score belongs to MDASH running MAI-Cyber-1-Flash alongside GPT-5.4, not to the new model by itself. CyberGym Level 1 is a known-vulnerability reproduction test. It gives an agent a vulnerability description and the corresponding unpatched source code, then checks whether it can produce a working proof of concept. It does not measure blind vulnerability discovery or whether a generated patch is correct. CyberGym's public leaderboard did not list Microsoft's 95.95% result when checked on July 28, 2026. It still listed Microsoft's May 12 MDASH submission at 88.4%. Microsoft's public materials do not say whether the result was submitted for listing. Microsoft's earlier 96.55% MDASH result does not resolve the comparison. That June figure counted any crash, including non-target vulnerabilities. The July materials do not say whether the 95.95% result uses the same criterion, so the two scores cannot safely be read as a before-and-after performance trend. According to Microsoft's model card, MAI-Cyber-1-Flash is a sparse mixture-of-experts transformer with 137 billion total parameters, five billion active parameters, and a 256,000-token context window. It is a cybersecurity fine-tune of MAI-Code-1-Flash, which was developed from a MAI-Thinking-1 mid-training checkpoint. The model card says the evaluated configuration replaced 80% of MDASH's existing models and raised the reported CyberGym result from 88.4% to 95.95%. That 80% figure is the share of models replaced. The separate 90% figure is the maximum share of tasks Microsoft says the smaller model can handle. Taken together, the disclosed design points to routing as the central technical claim: MAI-Cyber-1-Flash is intended to handle most tasks, GPT-5.4 takes the hardest remainder, and Microsoft reports the outcome at the MDASH system level. Microsoft's launch announcement defines the 50% saving against its current best MDASH model mix of GPT-5.4, GPT-5.4 mini, and GPT-5.3 Codex. The product page separately describes the system as delivering "comparable performance at 50% of the cost of leading models." The announcement and model card do not disclose the token use, call volume, latency, task mix, or compute allocation behind that comparison, so the figure cannot yet be independently reproduced or normalised against other systems. "The model is one input, the system around it is the product." Taesoo Kim, Microsoft's vice president of agentic security, used that distinction when describing MDASH in June. Under a lightweight terminal harness, the model card reports scores of 0.314 on CVEBench, 0.553 on CyberSecEval4 threat intelligence, 0.33 on its malware-analysis test, and 0.651 on CRSBench at POV=1200. The model scored zero across the kernel, userspace, and browser categories of ExploitGym, which asks agents to turn supplied vulnerabilities and crashing inputs into working code-execution exploits. Those results come from different tasks and scoring scales, so none is a standalone CyberGym score for MAI-Cyber-1-Flash. Microsoft said all benchmark testing took place in a network-isolated environment with no access to production systems, the public internet, or external services. The model card also warns that generated text and code may be inaccurate or incomplete and should be reviewed before consequential use. Software vulnerability management using MAI-Cyber-1-Flash inside MDASH is the first scenario Microsoft has announced for Project Perception, its broader system for coordinating defensive security agents. Project Perception is scheduled to enter public preview on August 3, with Microsoft planning to extend the model beyond software vulnerability work to additional security workflows.

Microsoft touts cost-saving AI model for cybersecurity

Microsoft on Monday talked up a new artificial intelligence model for spotting cybersecurity vulnerabilities. The move marks the company's first major push to rejuvenate its cybersecurity business since it brought back Google executive Hayete Gallot to run the unit. When paired with OpenAI's general-purpose GPT-5.4, Microsoft's MAI-Cyber-1-Flash outperforms Anthropic's Mythos 5, Google's 3.5 Flash Cyber and OpenAI's GPT-5.5 Cyber on the CyberGym benchmark, the company said. "We have world-leading performance at 50% of the cost," Mustafa Suleyman, CEO of Microsoft AI, said at an event the company held in San Francisco. The generative model is the software maker's first for cybersecurity. It will work in its Project Perception, a collection of AI agents for discovering and fixing weaknesses that becomes available in public preview starting Aug. 3, according to a blog post from Gallot. She rejoined Microsoft in February to become executive vice president of security, as its top leader in the category, former Amazon cloud executive Charlie Bell, became an individual contributor. Generative AI models have made it easier for attackers to quickly try to exploit newly documented vulnerabilities. Anthropic and OpenAI have released models that can help cybersecurity practitioners with defense. So far in 2026, Microsoft shares have come down 19%. "Given that consensus view that open-source (Chinese and other) AI models are poised to take share from the frontier labs, investor sentiment about Microsoft's high OpenAI exposure has swung back to being perceived as a risk," Microsoft analysts led by Karl Keirstead wrote in a Sunday note to clients. Keirstead recommends buying the stock. This year the company has announced its own model that can generate code in the GitHub Copilot tool, and lately it's been drawing on a first-party model in the Excel spreadsheet program. While Microsoft CEO Satya Nadella has a partnership with OpenAI to maintain, but he's also been allocating computing power to train models in house, with an eye toward spending efficiency. "By combining specialized models and data with the right agents, tools, security context, and harness, we can advance the frontier of cost to outcome," Nadella wrote in a Monday X post. Microsoft hasn't disclosed the scale of its cybersecurity business since 2023, when it said annual revenue exceeded $20 billion. In 2023, Microsoft introduced the Security Copilot assistant for cybersecurity practitioners that incorporated OpenAI's GPT-4. The service now comes with Microsoft's two most high-end productivity software bundles. Project Perception can suggest and implement code changes once given permission, and it can connect with non-Microsoft products. Cybersecurity executives "look at this as maybe a way to lower the bar and be able to bring in more talent to actually staff the SOCs and and get more people to participate because right now it's very limited in the industry," Gallot told CNBC. Companies maintain security operating centers (SOCs) full of people who look out for threats to information-technology systems. Last week, OpenAI said its models exploited a vulnerability and attacked AI startup Hugging Face's infrastructure during a test. Hugging Face used a model from Chinese lab Z.ai to conduct forensic analysis. "I think it's a great illustration of why you need to defend with AI against the bad guys who have AI, right?" Gallot said. There's plenty of room for Microsoft to improve the performance of the new model. "We have a unique data set," Suleyman said in an interview. "We've used way less than 1% of that data."

Microsoft built an agentic security system with red, blue, and green team AI agents. It enters public preview August 3.

Microsoft launched Project Perception, an agentic security system with red/blue/green AI agents. Its MAI-Cyber-1-Flash model scores 96% on CyberGym (+12 over Mythos) at 50% lower cost. Public preview August 3. Microsoft announced Project Perception on Monday, an agentic security system that coordinates three classes of AI agents in a continuous loop: red team agents that find vulnerabilities before attackers do, blue team agents that investigate and assess which risks are meaningful, and green team agents that fix defences across the environment. The system enters public preview on August 3. Microsoft described it as a "new Cyber Stack" built for a world where AI-powered attacks move faster than human defenders can respond. The first concrete benchmark: Microsoft's custom MAI-Cyber-1-Flash model, deployed inside its MDASH software vulnerability management system, scores 96% on CyberGym, an industry benchmark for vulnerability assessment. That is 12 points above Anthropic's Mythos, currently the most capable frontier model for cybersecurity tasks. Microsoft also claims the configuration delivers nearly 50% cost savings versus the current MDASH setup in production, by matching the right model to the right task rather than routing everything through a single expensive frontier model. Project Perception uses a multi-model architecture rather than relying on one model for everything. Frontier models handle complex reasoning. Specialised cyber models handle high-volume, low-latency tasks. The system draws on Microsoft's visibility across identities, endpoints, applications, data, clouds, and AI systems, and can take action across those environments, not just generate alerts. Microsoft's AI already found a record number of vulnerabilities in its own software, and Project Perception extends that capability to customers' environments with agents that operate continuously rather than in monthly patch cycles. The competitive shot at Anthropic is deliberate. Mythos became the default cybersecurity model after the White House temporarily blocked it, generating enormous attention for its offensive capabilities. Microsoft is now saying its own specialised model outperforms Mythos at half the cost, using training data from decades of defending enterprise environments that Anthropic does not have. The White House launched Gold Eagle this month to coordinate AI-powered cyber defence, and Project Perception is Microsoft's bid to be the platform that defence runs on. Public preview August 3 means enterprises can test it. Whether the 96% benchmark holds against real-world attacks rather than synthetic evaluations is the question the preview is designed to answer.

Microsoft escalates the AI security race with 'Project Perception' and a new in-house model

Microsoft on Monday unveiled Project Perception, an AI cybersecurity system built to defend against AI-driven attacks, aiming to keep pace with both hackers and its technology rivals. The system, which enters public preview Aug. 3, coordinates three sets of AI agents: red team agents that hunt for paths an attacker could take, blue team agents that determine which risks matter and green team agents that make fixes. It's based on MAI-Cyber-1-Flash, a new AI model designed specifically for cybersecurity, which the company says does most of the work of larger models at half the cost. It runs in conjunction with OpenAI's GPT-5.4, which Microsoft reserves for the 10% of tasks it calls exceptionally hard. Microsoft says the combination scores 96% on CyberGym, a benchmark measuring how well AI systems find real vulnerabilities in large codebases. The company did not give the model to independent testers before releasing it, according to The New York Times. Microsoft says the model was independently assessed by a third party. The model is available at launch only to customers of MDASH, Microsoft's AI-powered tool for finding vulnerabilities in code. Microsoft CEO Satya Nadella said in a post on X that the initiative is an example of how the company can get better results per dollar by not locking its security systems to a single AI model family. "This is the benefit of building the harness, context/signals, and action space separate from one model family," he wrote. "By combining specialized models and data with the right agents, tools, security context, and harness, we can advance the frontier of cost to outcome." The initiative was announced Monday morning at an event in San Francisco by Hayete Gallot, the EVP for Microsoft Security, joined by colleagues including Mustafa Suleyman, CEO of Microsoft AI. In a blog post, Gallot wrote that security needs a new "Cyber Stack," and that approaches built for a world of human actors cannot keep pace with AI, agents and machine-speed attacks. In an interview last week for GeekWire's Microsoft 2.5 series, Gallot said that MDASH was effectively Microsoft's first step into agentic security. No system can reason directly over 100 trillion signals a day, so Microsoft is distilling them into a graph that agents can navigate, Gallot said, routing each threat to whichever model handles it best. In practice, this means software can quarantine a device or cut off access on its own. The announcement comes days after OpenAI disclosed that two of its AI models broke out of a testing sandbox and hacked into Hugging Face, the AI development platform. Rivals have been more cautious, under government restrictions. Two of the four systems Microsoft benchmarked against, Anthropic's Mythos 5 and OpenAI's GPT-5.6 Sol, are limited to small groups of government-approved customers.

Microsoft wants AI agents fixing bugs before hackers find them

Why it matters: Cybersecurity and AI companies are racing to help defenders keep pace with attackers using increasingly capable AI models to automate hacking. Driving the news: Microsoft unveiled its anticipated Project Perception platform at an event Monday in San Francisco. * Project Perception is a security platform built around specialized AI agents that share security intelligence and work together to mirror the jobs performed by human security teams. * The company introduced three initial agents -- Red, Blue and Green -- that can identify vulnerabilities, determine which flaws pose the greatest risk and write and deploy software patches. * Microsoft also showed off MAI-Cyber-1-Flash, the first security model that the company has trained in-house. * The model performs roughly 95% of the work done by Microsoft's MDASH vulnerability-finding system. More computationally intensive tasks are routed to GPT-5.4, David Weston, Microsoft's corporate vice president of AI security, told Axios. The intrigue: Microsoft said combining MAI-Cyber-1-Flash with GPT-5.4 allows the system to achieve a 95.95% score on the CyberGym benchmark, which measures a model's ability to generate working proof-of-concept exploits for known software vulnerabilities. * By comparison, competing models, including Anthropic's Mythos and OpenAI's GPT-5.5-Cyber, scored around 83%. * Rather than releasing the model publicly, Microsoft will make MAI-Cyber-1-Flash available through Azure AI Foundry, using the company's existing customer vetting and GPU provisioning process, Weston said. Between the lines: Weston said Microsoft intentionally built the smaller, specialized model to handle most vulnerability analysis, reserving larger frontier models only for the most difficult tasks, to reduce the cost of scanning large software repositories. The big picture: Security vendors are increasingly rolling out specialized cyber models as organizations grapple with AI-driven attacks that can dramatically increase the speed of vulnerability discovery and exploitation. Microsoft follows similar announcements from Google and Cisco over the past week. * "We're not going to let the attackers have all the productivity increase," Weston said. Yes, but: Defenders remain cautious about turning security work over to autonomous AI agents. * Weston said Microsoft expects organizations to gradually build trust in the technology before allowing agents to operate with greater independence, adding that the company still has to "earn the right" to make those systems more autonomous. What's next: Microsoft said public preview of MAI-Cyber-1-Flash begins next week, and the company plans to expand Project Perception with additional specialized security agents over time.

Microsoft launches AI cybersecurity model, agentic defense platform to cut enterprise security costs

Microsoft opened a new front in the AI security wars on Monday, unveiling its first custom-built cybersecurity model and a sweeping agentic defense platform -- and making an argument that could reshape how enterprises buy AI: the future belongs not to the biggest model, but to the cheapest one that's good enough, routed intelligently. The company announced MAI-Cyber-1-Flash, a compact security model developed in-house by its Microsoft AI (MAI) division, embedded inside MDASH, Microsoft's multi-agent harness for finding and fixing software vulnerabilities. Together, the company says, the system scores 96% on CyberGym -- a benchmark measuring how well AI systems reason over large codebases to find real vulnerabilities -- beating frontier models including Mythos, Gemini, and GPT, while cutting costs roughly in half compared to Microsoft's own current production configuration. Alongside the model, Microsoft introduced Project Perception, an agentic security system that coordinates "red team" agents that hunt for paths to compromise, "blue team" agents that investigate and triage risk, and "green team" agents that remediate and harden defenses. Project Perception enters public preview on August 3. In an exclusive interview with VentureBeat, Microsoft AI CEO Mustafa Suleyman made clear the company sees Monday's announcement as the opening move in a much longer campaign. "We really do have a pretty significant data and harness and expertise moat, and that is enabling us to train models which are faster, better, cheaper, and I think this is genuinely the tip of the iceberg," Suleyman said. "We haven't been working on this for long. The next model is going to be pretty phenomenal." Inside the 90/10 architecture that still depends on OpenAI's GPT-5.4 The most technically revealing detail in the announcement is not the model itself but how Microsoft deploys it. MAI-Cyber-1-Flash was designed to handle up to 90% of security tasks efficiently, while MDASH escalates the remaining 10% of exceptionally difficult problems to a larger frontier model -- which, notably, is OpenAI's GPT-5.4. In other words, Microsoft's flagship security AI still leans on its longtime partner-turned-rival for the hardest work. Asked to explain that relationship, Suleyman pointed to the harness, the orchestration layer that routes each incoming problem to the right model. "The harness is like a router," he told VentureBeat. "It's kind of like guardrails and a rule set of an organizing logic, which matches queries to... incoming problems to a model that suits the problem." The system has three components, he explained: the harness, the small and fast MAI-Cyber-1-Flash handling the bulk of queries, and GPT-5.4 sitting alongside as "just a generalist coding model." Pressed on how a system reliant on OpenAI's model can outperform frontier competitors, Suleyman argued the performance comes from the whole system, not any single model. "These are very complicated, long, agentic loops which require storing state, drawing on another database, consulting best practice... handing back to a small model, writing a bunch of code, validating that that was correct," he said. "There's like hundreds of steps to solve that, and that's why it's really the system together that delivers the better performance." And why GPT-5.4 specifically for the escalation tier? Cost, again. "GPT-5.6 is expensive. GPT-5.4 is incredibly good relative to its cost," Suleyman said. "The whole game here is to reduce the costs. Mythos and so on are extremely expensive models... we want to be able to deliver better performance for cheaper. That's what customers want." The arrangement captures Microsoft's evolving posture toward OpenAI: still a customer of the partnership that drew regulatory scrutiny in Brussels and Washington in 2024, but increasingly determined to own the layers of the stack where it believes it holds durable advantages. Why token costs -- not model quality -- are becoming the real barrier to enterprise AI adoption The economics may matter more than the benchmark. Microsoft says the new configuration delivers roughly 50% cost savings against the current MDASH setup, which runs a blend of GPT-5.4, 5.4 mini, and 5.3 codex. In security -- an always-on workload processing enormous volumes of signals -- token costs compound relentlessly, and Microsoft argues they have become the binding constraint for defenders. Suleyman frames the cost issue as downstream of a harder physical limit. "The key barrier to adoption is access to chips, and cost is a function of chips," he said. "No matter how much money you've got, there's actually a limited supply of chips. Then trying to squeeze more model output on fewer chips is clearly super valuable." He also described a broader enterprise backlash against frontier-model pricing. Companies initially maxed out on the best available models, he said, but "then they realize they're sort of paying... a phenomenal amount of money, and people are absolutely token maxing everywhere across their business. So there's a massive pushback to reduce cost everywhere." That positions Microsoft to ride a market trend rather than fight it. Cost-efficient, near-frontier models have proliferated over the past year -- from xAI's recent Grok release to a wave of Chinese models built on the same premise -- and Microsoft is betting that as a platform company it can align itself with enterprise cost pressure. "The top model providers want you to use the most expensive model continuously, whereas because we are a platform, we're on the side of the enterprise," Suleyman said. "There's no point asking... Mythos what the capital of France is." The 100-trillion-signal data moat Microsoft says no competitor can replicate Every AI lab claims differentiation. Microsoft's claim in security rests on something genuinely hard to copy: telemetry. The company processes more than 100 trillion security signals daily -- a figure consistent with its 2025 Digital Defense Report, which also cited 4.5 million new malware files blocked and 5 billion emails screened per day -- and draws operational insight from 1.6 million customers. "We have trillions and trillions of data points going back decades," Suleyman said. "It is, I think, the largest longitudinal cybersecurity dataset around," in part because Microsoft's customer base includes governments "who have been consistently attacked for years, and we have been consistently attacked." Asked directly whether this constitutes an advantage no competitor can match, Suleyman didn't hedge: "That is definitely a moat for us. Both the data and the expertise, and just the experience in the institution of going through that process." The strategic logic is that cybersecurity functions as a live reinforcement-learning loop: defenders act, outcomes are observed, models improve. Microsoft argues that connecting actions to outcomes -- what was exploited, what was contained, what was blocked -- yields training signal that pure model labs simply cannot buy or manufacture. There is real substance here, but the usual caveats apply. The CyberGym results come from Microsoft's own evaluation, the fine print shows the headline "96%" is actually 95.95%, and vendor-run benchmarks that pit an entire tuned agentic system against competitors' base models are not apples-to-apples comparisons. What Microsoft has measured is a full harness-plus-models configuration against what customers might otherwise assemble -- arguably the commercially relevant comparison, but not a controlled model-versus-model test. The dual-use dilemma: how Microsoft plans to keep a vulnerability-hunting AI out of the wrong hands A model built to find challenging vulnerabilities in complex codebases is, by definition, a model that could find vulnerabilities for attackers. This is not a theoretical concern. Microsoft's own threat intelligence team, in joint research with OpenAI published in February 2024, documented nation-state actors from Russia, North Korea, Iran, and China probing large language models for reconnaissance, scripting, and vulnerability research. Its 2025 Digital Defense Report went further, warning that AI agents could eventually automate the entire attack lifecycle. Suleyman said Microsoft is gating access accordingly. "We're very strict about who gets access to the model, and we're very careful about that," he said. "We constantly monitor the API and usage." An approved user, he added, "has to be seen to be having good intent, but also have technical competence." The rollout will be deliberately staged: "It's not going to be thousands next week. There will be tens, and then hundreds, and then thousands." Microsoft says the model was evaluated by its AI Red Team, subjected to automated and expert-led adversarial exercises, and independently assessed by a third party, with deployment wrapped in tenant isolation, auditing, and sandboxed execution environments with no internet access. Suleyman also offered a candid acknowledgment of Microsoft's positioning relative to the bleeding edge -- one that doubles as a pitch to risk-averse buyers. "Even though we might be a few months behind the absolute cutting edge at any given moment... it matters that we're doing it very carefully and thoughtfully, and we have a track record of doing that," he said. For a company that spent 2024 absorbing hard security lessons -- from delaying its Recall feature over privacy concerns to convening an industry summit after the CrowdStrike outage disabled some 8.5 million Windows devices -- that trust-first framing is both strategy and necessity. What Microsoft's superintelligence roadmap signals about the future of enterprise AI Suleyman described a rapidly accelerating MAI roadmap, roughly nine months after Microsoft stood up its superintelligence team. "We have the compute that we need. We certainly have the data we need. We have the talent," he said. "Our momentum is accelerating rapidly." The top enterprise demand he's hearing is for "agents that can produce arbitrary code to solve whatever problem they direct them at," as vibe-coded internal tools graduate from experiments into production. The next phase, he said, pulls voice, transcription, image, and coding models "all integrated into the same harness." Notably, Suleyman expressed skepticism about the industry's default assumption that everything eventually converges into one giant unified model. "It remains to be seen whether one giant model that is fully multimodal is actually able to deliver additional transfer learning benefit because of the integration," he said, "or whether it's just a big lumbering expensive giant." That skepticism is the through line of the entire announcement. Microsoft is wagering that the unit of competition in enterprise AI is no longer the model at all -- it's the system: the router, the specialized small models, the frontier fallback, and the proprietary data feeding the loop. In security, where Microsoft controls both the telemetry flowing in and the products that act on it, that wager is at its strongest. Whether it holds in domains where the company's data advantage is thinner remains the open question hanging over the MAI roadmap. For now, though, Microsoft has offered the industry a preview of how it intends to fight the next phase of the AI race: not by building the biggest brain, but by building the best machine around it. As Suleyman put it, this is the tip of the iceberg -- and Microsoft is betting everything on what sits below the waterline.

Microsoft launches MAI-Cyber-1-Flash cybersecurity AI model

MAI-Cyber-1-Flash, paired with GPT-5.4 inside Microsoft's MDASH harness, outperforms models from Anthropic, Google, and OpenAI on a key benchmark Microsoft $MSFT launched MAI-Cyber-1-Flash, its first cybersecurity-specialized AI model, on Monday at an event in San Francisco, alongside a new agentic security platform called Project Perception. The company says the model, when combined with OpenAI's GPT-5.4 inside its MDASH vulnerability management harness, delivers 96% on the CyberGym benchmark -- 12 percentage points above Anthropic's Mythos -- at 50% of the cost of Microsoft's current MDASH configuration. MAI-Cyber-1-Flash is intended to shoulder the bulk of routine security work -- roughly 90% of tasks -- leaving GPT-5.4 to handle only the most demanding cases, the company said. The model is derived from the MAI-Thinking-1 lineage and was built in-house, and Microsoft says it draws on more than 100 trillion daily security signals across identity, endpoint, cloud, and network. "We have world-leading performance at 50% of the cost," Mustafa Suleyman, CEO of Microsoft AI, said at the event, according to CNBC. Project Perception, described in a blog post by Hayete Gallot, Microsoft's executive vice president of security, coordinates three classes of specialized agents. Red team agents model how adversaries might move through a system, blue team agents focus on identifying and prioritizing active threats, and green team agents carry out remediation steps to close gaps in defenses. The platform enters public preview on August 3. Dave Weston, the lead engineer for Perception, said the platform collapses work that previously took hours across multiple specialists. "We've gone from this taking hours and hours of manual work from multiple specialized folks across the security organization -- appsec hunters, remediation engineers, you name it -- and in minutes, we have a fix for all of this," Weston told TechCrunch. Gallot, who returned to Microsoft in February to oversee the security division, framed the platform as giving enterprises the means to "defend against AI with AI at the scale and speed that the attackers have," according to TechCrunch. Microsoft unveiled a family of seven in-house AI models in June, including the MAI-Thinking-1 reasoning model from which MAI-Cyber-1-Flash is derived, as the company has moved to reduce reliance on outside model providers including OpenAI. Microsoft last reported its cybersecurity revenue figures in 2023, putting them above $20 billion annually, and has not updated that figure since. The new tools enter a market that already includes Anthropic's Mythos, which reached a limited set of partners via a program called Glasswing, and OpenAI's own offering, which debuted in May under a program called Daybreak.

Microsoft Says MDASH Beats Claude Mythos and GPT-5.6 Sol in Cybersecurity Test

The scanner is in private preview through Microsoft Defender, where teams can review findings and generate proposed fixes. Microsoft has released its first dedicated cybersecurity model named MAI-Cyber-1-Flash and plugged it into MDASH, a vulnerability-hunting system that it says beats Anthropic's Mythos 5 and OpenAI's GPT-5.6 Sol while costing 50% less than Microsoft's current best MDASH configuration, according to the company. The combined setup scored 95.95% on CyberGym, according to Microsoft. CyberGym is a benchmark that asks AI agents to reproduce 1,507 known vulnerabilities across 188 open-source projects, then scores them by the percentage successfully reproduced in a controlled environment. That put MDASH ahead of GPT-5.5 Cyber at 85.6%, Mythos 5 at 83.8%, GPT-5.6 Sol at 83.6%, and Gemini 3.5 Flash Cyber at 83.2%. The result is self-reported by Microsoft and had not appeared on CyberGym's public leaderboard at publication time, though the benchmark uses a public test set and a defined success metric. MAI-Cyber-1-Flash does not work alone. Microsoft says it handles up to 90% of tasks, while MDASH routes the hardest 10% to GPT-5.4. That matters because tokens -- the chunks of text an AI processes -- cost money every time a model reads code or produces an answer. This is the first time a Microsoft model built for efficiency is capable of beating a dense state of the art model built with general capabilities in mind. "When combined with MDASH, (MAI-Cyber-1-Flash) delivers world-class performance at 50 percent of the cost of leading models," Microsoft CEO Satya Nadella wrote. A model (in this case MAI-Cyber-1-Flash) is the AI that reasons over the code. A harness (MDASH in this case) is the machinery around it: the agents, tools, checks, and workflow that decide where to look, challenge suspected findings, remove duplicates, and prove that a bug can be triggered. MDASH uses more than 100 specialized agents assigned to audit code, debate whether a finding is genuine, and build a proof of concept -- a working demonstration that the flaw exists. Microsoft said occasional scans and delayed patches are becoming obsolete as AI makes bug discovery cheaper. The company argues that decades of security data give it an advantage, adding, "No one can manufacture this history." Ever since the release of Claude Mythos, cybersecurity experts have been trying to beat or match its capabilities. Researchers reproduced Mythos-style vulnerability hunting with public models for under $30 per scan. Dawid Moczadło, one of the researchers involved, said "the moat is moving from model access to validation." The scarce part is becoming the system that proves findings without burying developers under false alarms. Decrypt also reported that GPT-5.5 Cyber had recently taken the public CyberGym lead with an 85.6% score, narrowly beating Mythos. Microsoft's score is about 10 points above GPT-5.5 Cyber and 7.5 points above MDASH's previous result, but it evaluates the full system rather than MAI-Cyber-1-Flash by itself. Microsoft is putting MDASH into private preview through Microsoft Security Exposure Management in the Defender portal. Customers can scan Git repositories, see findings ranked from unlikely to proven, and use the Defender CLI to generate proposed code fixes for developer review. The preview currently limits repositories to roughly 256MB and permits one concurrent scan per tenant. Project Perception is expected to extend the same multi-agent approach beyond code scanning into broader threat monitoring and remediation workflows.

Microsoft's first cybersecurity model powers new Project Perception agents

Microsoft Corp. today introduced its first in-house cybersecurity model, MAI-Cyber-1-Flash, and a companion agentic system called Project Perception that fields teams of artificial intelligence agents to probe for weaknesses, investigate threats and remediate them, starting with software vulnerability management. The model is a compact, code-tuned derivative of Microsoft's MAI-Thinking-1 line, trained in-house on the company's own exploit and remediation records. Microsoft said it carries roughly 90% of the workload inside MDASH, the multi-model agentic scanning harness Microsoft detailed in May, and route the hardest 10% to OpenAI Group PBC's GPT-5.4. Microsoft said that split cuts the cost of running the harness by about half. On the public CyberGym benchmark, which covers 1,507 vulnerability reproduction tasks, MDASH running on MAI-Cyber-1-Flash scored 95.95%, according to Microsoft. The company put Anthropic PBC's Mythos at about 84% on the same test. MDASH scored 88.45% when Microsoft disclosed the harness. That earlier version found 16 previously unknown flaws in Windows networking and authentication components, four of them critical remote code execution bugs, all fixed in May's Patch Tuesday release. Perception divides its agents into three groups. Red agents probe for weaknesses the way an attacker would, blue agents investigate signals and rank what actually matters and green agents write and deploy fixes. High-impact actions still require human sign-off. Microsoft is pricing the system on consumption, metered in what it calls Security Compute Units. "Project Perception brings together signals, context, models and specialized agents into a continuously learning system of defense," Hayete Gallot, executive vice president of Microsoft Security, wrote in the company's announcement. "It can reason, prioritize and act at machine speed while keeping humans firmly in control." Chief Executive Satya Nadella said that when combined with MDASH, the model "delivers world-class performance at 50 percent of the cost of leading models." Microsoft is not first out of the gate. Anthropic previewed Mythos in April, Google LLC launched Gemini 3.5 Flash Cyber last week and Cisco Systems Inc. has pushed a security model of its own. "We're not going to let the attackers have all the productivity increase," David Weston, Microsoft's corporate vice president of enterprise and OS security, told Axios, adding that the company still has to "earn the right" to grant its agents more autonomy. Perception enters public preview on Aug. 3, initially inside Microsoft Defender. The company said it will extend the system across its wider security portfolio over time. MAI-Cyber-1-Flash becomes available through Azure AI Foundry on the same date, subject to Microsoft's existing customer vetting process.

Microsoft unveils its first in-house cybersecurity AI model

Microsoft on Monday introduced MAI-Cyber-1-Flash, its first in-house cybersecurity model, and Project Perception, an agentic security system that will enter public preview on Aug. 3. Microsoft said MAI-Cyber-1-Flash is embedded in MDASH, its multi-agent system for finding and fixing software vulnerabilities, and that the setup scored 96% on the CyberGym benchmark while cutting costs by about 50% from its current production configuration. The company said the benchmark result exceeded frontier models including Mythos, Gemini and GPT. The current MDASH setup uses a mix of GPT-5.4, GPT-5.4 mini and GPT-5.3 codex. Project Perception coordinates red team agents that search for paths to compromise, blue team agents that investigate and triage risk, and green team agents that remediate and harden defenses, Microsoft said. In an interview with VentureBeat, Microsoft AI CEO Mustafa Suleyman said the announcement was the start of a broader push. "We really do have a pretty significant data and harness and expertise moat," he told VentureBeat. Suleyman said MAI-Cyber-1-Flash was built to handle up to 90% of security tasks, while MDASH sends the remaining 10% of harder problems to OpenAI's GPT-5.4. He described the harness as a router that matches incoming problems to the model best suited to solve them. Asked why Microsoft uses GPT-5.4 for the escalation tier, Suleyman cited cost. "GPT-5.6 is expensive. GPT-5.4 is incredibly good relative to its cost," he told VentureBeat. He said enterprise customers were pushing back on frontier-model pricing as token costs rise. "The top model providers want you to use the most expensive model continuously, whereas because we are a platform, we're on the side of the enterprise," Suleyman said. Microsoft said it processes more than 100 trillion security signals a day and draws on data from 1.6 million customers. Its 2025 Digital Defense Report cited 4.5 million new malware files blocked and 5 billion emails screened per day. Suleyman said that telemetry gives Microsoft a competitive advantage. "That is definitely a moat for us," he said, referring to the company's data, expertise and operating experience. Microsoft said access to the vulnerability-hunting model will be tightly controlled because the system could be misused by attackers. Suleyman said the company will monitor API use and roll out access in stages, starting with tens of users, then hundreds, then thousands. The company said the model was evaluated by Microsoft's AI Red Team, tested through automated and expert-led adversarial exercises, and assessed by an independent third party. Microsoft said deployment includes tenant isolation, auditing and sandboxed execution environments with no internet access. Suleyman also said he was skeptical that enterprise AI would converge on a single large model. "It remains to be seen whether one giant model that is fully multimodal is actually able to deliver additional transfer learning benefit because of the integration, or whether it's just a big lumbering expensive giant," he told VentureBeat.

Microsoft unveils AI cybersecurity tools

SAN FRANCISCO -- OpenAI sent shock waves across Silicon Valley last week when it revealed that two of its artificial intelligence technologies had gone rogue and hacked into a popular internet library. The incident showed the unpredictable power of new AI systems and the growing importance of technology that can be used against AI attacks. On Monday, Microsoft added to the widening assortment of AI security tools with the release of systems designed to help businesses protect their computer networks. It is among a number of companies racing to take the same kind of advanced systems used to commit attacks and instead harness their power to prevent hacks. They are hoping to capitalize on rising concerns that AI presents a serious threat to computer infrastructure that security experts are just starting to come to grips with. Microsoft executives are among the many voices in tech arguing that powerful cybersecurity systems should be more widely distributed so that more organizations can better defend themselves. That viewpoint runs counter to growing concerns among some tech executives and officials in Washington that new AI systems need to be carefully monitored or kept away from the public because they can be such effective hacking tools. "The cat is out of the bag," said Hayete Gallot, an executive vice president at Microsoft who oversees the company's security efforts. Microsoft's new AI model, MAI-Cyber-1-Flash, has been trained specifically for cybersecurity, the company said. The system learned its skills partly by analyzing decades of data that Microsoft collected when responding to hacking incidents experienced by its customers. Microsoft has unusual access to security data because its products, such as the Windows operating system, Outlook email and Azure cloud computing, are so widely used and are frequent targets of cyberattacks, said Mustafa Suleyman, who oversees the development of Microsoft's AI models. Unlike other leading AI companies, Microsoft did not share the new model with independent testers for evaluation before releasing the technology. But the company said it expected that the model integrated into its security tools would top the leaderboard for a standard benchmark test called CyberGYM after it was released Monday, surpassing offerings from OpenAI and Anthropic. Microsoft said the price of using the new system was about half the price of other leading technologies, which are more expensive partly because they were developed to do many things, not just cybersecurity work. The company said it effectively handled 90% of queries, meaning the more expensive models would be necessary only 10% of the time. Suleyman said Microsoft had focused on lowering the costs so that the model could be deployed more quickly and more widely. MAI-Cyber-1-Flash will feed into another new Microsoft tool, Project Perception. It transforms various AI models into teams of "agents" designed to find and repair network vulnerabilities. AI agents are digital assistants that can use other software to perform various tasks largely on their own. In some cases, Microsoft's agents will be able to imitate hackers. The AI security threat has emerged over the past year or so as AI companies have built systems that are particularly good at writing computer code. When Anthropic, for example, introduced an AI system in April, the company said it had used the technology to find thousands of security holes that had gone undetected in popular software systems for years. Arguing that the AI system was too dangerous for wide release, Anthropic limited the release of this enormously expensive technology to a small number of organizations so that they could use the technology to defend the internet's vital infrastructure. Not long after, OpenAI and Google said they, too, were sharing similar technology with only a group of partners. The White House also started to explore government oversight of such technologies. Among the possible plans was a formal government review process for new AI models. The field and norms are rapidly evolving. In an interview July 16, Microsoft's corporate vice president, David Weston, said it was "really important for us to make sure everyone has the capability." But when the product was announced Monday, the company limited the initial release to businesses and individuals who used a third Microsoft security tool, MDASH, which is designed for finding and closing security holes in software. As companies try to control the use of their latest models, experts have shown that other technologies could help drive malicious attacks on computer networks. Last month, researchers at the University of Toronto said they had found a way to use freely available AI technologies to create a dangerous computer "worm" capable of targeting any known flaw in the world's computers. "A lot of these models are getting better," Weston said. "What concerns me is not the model du jour but that the capability is more widespread." In the weeks since, two Chinese startups have released technologies that are nearly as powerful as leading systems from Anthropic and OpenAI.

Microsoft Says Its New AI Beats OpenAI, Google, and Anthropic at Cybersecurity

The race to release the best and most powerful cybersecurity AI tool continues -- and Microsoft is the latest to throw down the gauntlet. The tech giant announced on Monday the release of a new cybersecurity-focused AI model that it claims outperforms the competition from Anthropic, Google, and OpenAI. Called MAI-Cyber-1-Flash, the model will operate within Microsoft's multi-model agentic scanning harness (MDASH) vulnerability platform. When combined with the capabilities of OpenAI's GPT-5.4, the model can offer industry-leading cybersecurity performance at half the price of a configuration that operates exclusively on OpenAI models, Microsoft claims. Microsoft's announcement follows a slew of new model releases from frontier labs -- each touted as more frightening and capable than the last. "Security is an always-on mission, and given the enormous volume of inbound attacks, token cost is now the real constraint for defenders," Microsoft detailed in a release. Microsoft's MDASH works by leveraging "the best models for each security task." In practice, this means it uses larger, more expensive models like GPT-5.4 for "10 percent of exceptionally hard tasks," whereas the new, more cost effective MAI-Cyber-1-Flash handles roughly 90 percent of others. That unified system is what Microsoft says scored a 96 percent on CyberGym, outranking Anthropic's Mythos by 12 points. (CyberGym is a framework for evaluating how well AI agents analyze and reproduce real-world security bugs). Alongside the new model, Microsoft also announced a new agentic security system called Project Perception. The system relies on three different types of agents. Red team agents act like a human red team, meaning they are authorized to use hacking tactics to identify vulnerabilities. Blue team agents examine the red team's findings to determine whether they pose meaningful risks, and green team agents work to take corrective action and strengthen defenses. Project Perception is also meant to be used within MDASH, and Microsoft says it will leverage the MAI-Cyber-1-Flash model. Project Perception will be available through public preview starting August 3. "Working together, these agents form a closed-loop system that continuously discovers, evaluates and improves an organization's security posture," a Microsoft announcement notes. As AI has grown more sophisticated, experts have begun warning that it will make sophisticated cyberattacks cheaper and easier to pull off. And there's been an accelerating AI cybersecurity arms race since about April, when Anthropic announced Project Glasswing. The initiative was meant to slowly roll out Claude Mythos, an AI model allegedly so powerful and dangerous that it could only be shared with trusted partners. Shortly after, OpenAI launched its answer to Glasswing, the Daybreak Cyber Partner Program, and then subsequently launched GPT-5.6, which it claims is its "strongest cybersecurity model yet." Microsoft launched its MDASH solution in May. Get 1 Smart Business Story delivered straight to your inbox when you subscribe to Inc.'s free daily newsletter.

Microsoft launches cybersecurity AI model to detect complex software vulnerabilities

In a blog post announcing the model, Microsoft said MAI-Cyber-1-Flash is designed to find challenging vulnerabilities in complex codebases. The model works with MDASH, Microsoft's multi-model agentic security system, to support advanced vulnerability discovery and security operations. Microsoft has launched MAI-Cyber-1-Flash, its first model built specifically for cybersecurity, as the company expands its specialised AI portfolio to address complex security challenges.In a blog post announcing the model, Microsoft said MAI-Cyber-1-Flash is designed to find challenging vulnerabilities in complex codebases. The model works with MDASH, Microsoft's multi-model agentic security system, to support advanced vulnerability discovery

Microsoft Launches Defense System to Combat AI-Powered Cyber Threats | PYMNTS.com

The new Project Perception turns signals into real-time protections and uses AI to defend against AI, the company said in a Monday (July 27) blog post. "Project Perception is based on a simple idea: effective defense requires continuous understanding of how an attacker sees the world, how a defender evaluates risk and how protections are improved over time," Microsoft said in the post. Perception coordinates three classes of specialized agents, including red team agents that identify potential vulnerabilities before an attacker can exploit them, blue team agents that investigate and determine what represents meaningful risk, and green team agents that take corrective actions and strengthen defenses, according to the post. "Working together, these agents form a closed-loop system that continuously discovers, evaluates and improves an organization's security posture," the post said. Project Perception uses a multi-model architecture that encompasses frontier and specialized cyber models and optimizes for quality and cost, per the post. "As part of this multi-model strategy, we are committed to bringing customers the best models for each security task, including innovating with our own specialized models," Microsoft said in the post. Together with agents and models, Project Perception includes signals and sensors, security context, a harness that coordinates the agents and models, and actuators that turn decisions into protection, according to the post. "Together, these layers create a continuous learning system that can understand risk, adapt to changing conditions and improve security outcomes over time," the post said. It was reported July 15 that Microsoft's cybersecurity business was developing more AI security products, cutting back on some of its more traditional security products and consolidating engineering teams. The report said Microsoft was making these changes to better respond to customer demand for solutions to the threat of AI-powered hacks, and to capture some of the spending that is going to AI firms Anthropic and OpenAI. The PYMNTS Intelligence report "Where Payments Decisions Happen: How Issuer Data Is Powering the Next Era of Commerce" found that 42% of issuers said AI has helped them save more than $5 million from fraud attempts in recent years.

Microsoft Launches "World-Class" AI Security Product at "Half-the-Cost

The company says its new product is as good as those from rivals, Anthropic, Google and OpenAI and it costs just half of what they charge Microsoft has launched a new AI security product called Project Perception. Powered by its new homegrown AI models specialized for cybersecurity tasks, the company has positioned it as a product that provides a cheaper alternative to Anthropic's Mythos, a model which garnered pre-launch publicity due to a mix of self-praise and global governmental concerns. The first specialised cybersecurity model comes alongside a new AI cybersecurity platform with which Microsoft is ostensibly taking a big swipe at the major AI players in the market - Anthropic, Google and OpenAI. Looks like the company is following up on CEO Satya Nadella's recent diatribe on AI giants calling out that a few big companies attempting to "eat up the economy". Microsoft described the MAI-Cyber-1-Flash as a model that's built "to find challenging vulnerabilities in complex codebases." One that is is built to animate MDASH, the company's harness dedicated to software vulnerability identification and remediation. "Together they deliver world-class performance at 50% of the cost of leading models." A blog post authored by Mustafa Suleyman, the company's AI czar and Hayete Galote notes that AI progress cyberthreats posed by AI has been as startling as progress in AI itself. Attackers have more powerful capabilities to probe a single weakness within a mountain of code. "As the cost of finding a flaw collapses, the old model of security, where you scan occasionally and patch eventually, is now obsolete," they say. For unlocking the true benefits of AI, we must first build outstanding cyber models that help all of us harden the software the world runs on, Suleyman says noting that this was the motivation behind MAI-Cyber-1-Flash, which has been built to find challenging vulnerabilities in complex codebases. It's been deeply integrated into MDASH, honed by the best cybersecurity experts in the industry and hardened across the largest security estate on the planet. This combined expertise delivers exceptional security protection, beating Mythos, Gemini and GPT on CyberGym, the gold standard benchmark for evaluating how systems reason over large codebases to find real vulnerabilities in the code, Microsoft says. Now that's something we haven't seen Microsoft do in a while - taking on existing rivals by name and claiming to be better than them (as the figures below show). The platform is called Perception (which we don't think is meant as a pun in any way) and deploys teams of agents to assist with and automate security workflows that includes identifying and fixing bugs.

Microsoft Introduces MAI-Cyber-1-Flash, Claims to Outperform Gemini, ChatGPT

Microsoft has introduced Project Perception, a new AI-powered cybersecurity platform, along with its first dedicated security model, MAI-Cyber-1-Flash, as the company steps up efforts to tackle increasingly sophisticated cyber threats. The technology giant claims its latest model delivers better vulnerability detection than competing AI models from Google and OpenAI while reducing operating costs by nearly 50%. The announcement reflects Microsoft's broader strategy of building specialized AI systems instead of relying solely on large, general-purpose models. As cyberattacks become more automated and AI-driven, the company believes security tools must evolve to detect, analyze and respond to threats faster than traditional methods.

Microsoft introduces MAI Cyber 1 Flash AI model, says it beats Mythos 5 on important benchmark

MAI Cyber 1 Flash AI is said to deliver "world-class performance" at 50 per cent of the cost of leading models. Microsoft has introduced its first cybersecurity AI model called MAI Cyber 1 Flash. The company says the model is built for complex codebases and can identify security flaws more efficiently at a low cost. Microsoft has also integrated the new model into its MDASH security platform. According to the tech giant, MAI Cyber 1 Flash even beats GPT 5.5 Cyber, GPT 5.6 Sol and Mythos 5 on CyberGym, a benchmark which is used to measure how well AI models find real security issues in large codebases. The tech giant also claims that MAI Cyber 1 Flash AI delivers "world-class performance" at 50 per cent of the cost of leading models. "MAI Cyber 1 Flash was designed to efficiently handle up to 90 per cent of all tasks, enabling MDASH to use the larger and most costly models in our fleet (in this case GPT 5.4) for the 10 per cent of exceptionally hard tasks that truly need them," Microsoft said. Also read: After Jensen Huang's open AI push, Anthropic CEO clarifies company's stance The company claims that MDASH with MAI-Cyber 1 Flash achieved a 96 per cent score on the CyberGym benchmark, which is 12 points higher than Mythos 5. Microsoft also announced Perception, a new agent-based security system that works alongside MDASH. It is designed to help security teams continuously monitor systems, patch software vulnerabilities and respond to new threats. The company says Perception will also use MAI Cyber 1 Flash for more security-related tasks in the future. The company also said that safety was a key focus while developing the model. MAI Cyber 1 Flash was tested by Microsoft's AI Red Team, evaluated through adversarial security exercises and independently reviewed by a third party. Also read: Sam Altman says AI has reached the singularity, calls it a turning point for humanity "Through MDASH, customers get enterprise-grade controls including Role-Based Controls, tenant isolation, encryption, auditability, and sandboxed execution environments with no internet access," the tech giant said. "The result is a cyber model that delivers powerful capabilities to defenders while maintaining the governance, security, and control enterprises expect from Microsoft."