Microsoft launches Project Perception with AI agents that outperform Mythos at half the cost

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 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 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.