Who Owns AI Risk? CIOs Split as 81% Lack Full AI Agent Oversight, Dataiku Survey Finds
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Who owns AI risk at work? Business and tech leaders can't agree, PwC survey finds - ZDNET
* PwC reveals business leaders can't agree on who is responsible for AI. * AI agents may lack the identity controls that manage employees today. * Is a dedicated AI executive or leader the answer? Artificial intelligence (AI) adoption continues to surge, with agentic AI and large language models
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IAM for AI agents: A Practical Enterprise Framework
What is IAM for AI agents? AI agents authenticate, invoke tools, and act across enterprise systems with delegated authority. IAM for AI Agents is the identity-control architecture that governs those actors. This guide covers the limits of conventional provisioning, the components that matter, how
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With the Rise of AI Agents, SOC 2 Should Adapt or Risk Irrelevance
Some compliance is mostly for show, and that's being charitable. You go through it for the website badge, not to find weak spots in your organization. SOC 2 is not like that. There's a reason your customers' procurement teams demand it. They want to know whether they can trust you with their data,
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Who's responsible for catching rogue AI agents? You are - ZDNET
* Misbehaving AI agents present a big risk to businesses. * Professionals must establish accountability for risks. * Take responsibility through guardrails and harnesses. It's been an interesting and concerning few months for professionals using generative AI. Beyond the release of ever-more
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'Almost nobody can fire an agent': CIOs and the AI agent oversight gap
A Dataiku survey of 685 CIOs finds gaps in AI agent oversight: 81% lack a full view of agents built outside formal channels. 72% can't confirm their agents deliver results. Most chief information officers cannot fully see the AI agents their staff build outside approved systems, a survey by
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Zero Trust for AI Agents Starts With Fixing Zero Visibility
The way we talk about AI agents is shifting, and the way we implement them requires an even more fundamental shift. While earlier discourse focused on how quickly organizations could stand up agents and how much productivity they could promise, a string of recent incidents, including a widely
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AI agent kill switch urged by Okta-led alliance - how businesses could make it work - ZDNET
* Okta, AWS, Google Cloud, Salesforce, and others form an AI agent security coalition. * The Alliance offers a blueprint for companies seeking visibility, control, and governance of agents. * AI agents need the equivalent of a kill switch to expeditiously terminate suspicious behavior. When a
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AI agents are the next thing endpoint management has to govern
AI agents now run on managed devices with the permissions of whoever launched them. Endpoint management built to track installed software now has to govern what those agents are allowed to do. Gartner forecasts that task-specific AI agents will be integrated into 40% of enterprise applications by
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Financial Services' next AI risk is the workflow nobody can explain
Financial firms need end-to-end workflow visibility to scale agentic AI Financial services is moving from AI that answers questions to AI that takes action - and that shift changes what accountability means. The Government's Financial Services Adoption Plan calls on the sector to move beyond
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AI agents need more than access control -- they need identity at runtime
Enterprises are adding AI agents, applications, workloads, and machine identities far faster than they add employees. Many now plan for a non-human population several times larger than their human one, and identity architecture is what decides how far that growth goes. Every AI agent needs a
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When the machine should stop and call a human | Fortune
Imagine an AI system you authorized for one narrow task quietly finding its own way into infrastructure you never gave it permission to touch -- not because anyone told it to, but because it hit a wall and improvised a path around it. Now imagine that instinct spreading across a swarm of AI agents,
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If an AI agent is attesting your controls, who's attesting the agent?
AI demands security governance to replace compliance-driven security theatre I've watched engineers lose a week before an audit collecting screenshots no one will ever look at again. I've seen incident response plans that exist on paper but have never been tested, and vendor questionnaires
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AI agents are exposing a security gap between the data they read and the systems they can change
Most conversations about AI security still orbit the model itself: Is the large language model (LLM) aligned, can it be jailbroken, does it hallucinate under pressure? Those are real questions. But as organizations move AI agents and LLM-powered workflows from pilot projects into production, a
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In the AI era, identity evolves into the control plane for trust
In the AI era, identity evolves into the control plane for trust Every major security shift ultimately comes down to one question: Who or what is allowed to do this? For most of the past two decades, the answer was a person with a username and password. In the artificial intelligence era, the
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AI agents are ready to act, but do they understand your business?
An AI agent approves a supplier payment. The invoice matches the purchase order, the amount sits within budget and the supplier is already in the system. Every check passes, but the decision is still wrong because the contract expired yesterday. That's the risk enterprises now face. An agent can
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Agentic security strategy faces a shrinking breach window
An agentic security strategy must account for software that can act on legitimate instructions in unexpected ways. Agents can use credentials, call tools and choose their own routes toward a goal, expanding the paths security teams need to watch. The risk arises even when no one instructs an agent
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Oracle CISO puts humans at the center of AI security governance
Oracle CISO puts humans at the center of AI security governance As enterprises face AI-driven cyberattacks, AI security governance must control autonomous systems that can reach data, connect applications and act on users' behalf. That makes human accountability a core security
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Access management gets a reboot as AI agents scale at Okta
Identity and access management is getting a second look as AI agents move from pilots into production. The rush to deploy them is exposing decades of stitched-together identity tools, integration costs and security gaps that enterprises can no longer ignore. Security teams face pressure to let the
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No Attacker Required: What a Two-Day Hackathon Taught Us About Agent Security
Two days, nineteen teams, one uncomfortable theme Most security research starts with an attacker. Ours kept finding trouble without one. In August, we gave our R&D teams two days and a single prompt: build the demo customers would ask to see twice. Hackathons like this one are a fixture of how we
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Agentic AI Governance: Policies, Compliance, Responsible AI
Accountability gets harder to trace. If an agent orders inventory, sends a client email, or approves a refund, someone still owns that outcome legally and financially. AI involvement does not remove an organization's responsibility for complying with applicable laws and regulations. Agentic AI
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AI agent identity security demands layered defenses, Omdia says
AI agent identity security demands layered defenses, Omdia says AI agent identity security is testing whether consolidated platforms can protect a growing class of machine identities. Palo Alto Networks Inc.'s acquisition of CyberArk Software Ltd. reflects broader consolidation in the identity
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Okta builds shared architecture for agent runtime security
Okta builds shared architecture for agent runtime security Agent runtime security requires controls across the technology stack as enterprises move AI agents into production. This week, Okta turned its agent security framework into a multivendor reference architecture through the Blueprint
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Securing data at the source in the age of agentic AI
Agentic-fueled attacks place focus on securing data at the source The practice of building cybersecurity directly into networks and databases has taken on greater importance in the new era of agentic AI. Trust boundaries are being relocated to the database itself, where access policies can be
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1Password limits AI agent access with task-based verification
AI agent access is complicating identity controls as agents log in, carry credentials and act on someone's behalf. They have characteristics of both human and machine users. That overlap can make actions harder to attribute: An agent may appear in an audit log as the person it works for, even
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1Password ties AI agent access to individual tasks
AI agent access is complicating identity controls as agents log in, carry credentials and act on someone's behalf. They have characteristics of both human and machine users. That overlap can make actions harder to attribute: An agent may appear in an audit log as the person it works for, even
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Agents expose the limitations of trust
Enterprise computing has long depended on chains of trust. Organizations trust their cloud providers, software vendors, identity systems and administrators to operate as designed. That model has worked because humans have remained the ultimate decision-makers. Agentic artificial intelligence
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Business leaders can't agree on who should own AI risk, a PwC survey reveals, while 81% of CIOs admit they lack complete oversight of AI agents built outside formal channels. The accountability gap comes as autonomous AI systems multiply faster than governance frameworks can manage them.
Business Leaders Split on AI Risk Ownership as Oversight Gaps Widen
A
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of approximately 4,000 business and tech leaders across 71 countries reveals a troubling reality: no single role has clear responsibility for managing AI agents or their security. The findings show 29% believe accountability sits with the CIO or CTO, 17% point to the CISO or cybersecurity teams, while 26% say it should rest with a dedicated AI leader. Another 11% admit accountability remains unclear, shared across multiple roles. This confusion comes at a critical time when4
and large language models now integrate into everything from enterprise applications to customer-facing chatbots, creating new entry points into corporate networks and expanding attack surfaces.
Source: SiliconANGLE
81% of CIOs Lack Complete AI Agent Oversight
The governance challenge extends beyond role confusion. A
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of 685 CIOs across eight countries found that 81% lack complete oversight of AI agents created outside formal channels. Even more concerning, 72% cannot consistently confirm whether their agents deliver the business outcomes they were built for, despite 90% claiming they have complete tracking of all agents. The Harris Poll conducted this survey between 9 to 29 July, covering CIOs in the US, UK, France, Germany, UAE, Japan, South Korea and Singapore. Florian Douetteau, co-founder and CEO of Dataiku, captured the problem succinly: "Monitoring tells you an agent is running. Managing tells you whether it's earned the right to keep running, and right now, almost nobody can fire an agent."Traditional IAM Systems Fall Short for Autonomous AI Systems
The root of the oversight problem lies in how identity and access management for AI differs fundamentally from human user management.
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typically work along two dimensions: lifecycle management and policy definition at design time, plus authentication and authorization at runtime. But these systems describe access as configured, not what an autonomous agent actually did with that access once inside an application. Non-human AI agents chain tasks, select tools dynamically, and compose actions that no entitlement review anticipated. OWASP's Top 10 for Large Language Model Applications identifies this as excessive agency (LLM06): an agent granted broad functionality exercises capability beyond its approved task. Static role assignment cannot bound that behavior, and configuration review cannot measure it.Compliance Frameworks for AI Struggle to Keep Pace
Existing compliance frameworks like
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face a similar adaptation challenge. Four core assumptions no longer hold: someone approves an account before it's spawned, every account has a known owner, the name in the log pinpoints the actor, and what an account can do tells you what it's expected to do. A recent study with Cloud Security Alliance found more than two-thirds of organizations cannot clearly distinguish AI agent actions from human ones. During an access review, a production database query at 10:03 am under a senior engineer's name might pass all controls, even though that engineer was getting coffee while their agent pushed updates to production. SOC 2's technology-neutral criteria can technically cover AI agents, but they don't explicitly require organizations or auditors to treat agents as a distinct identity class.Managing AI Risks Requires New Approaches to Lifecycle Management
The
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revealed that 67% of organizations estimate 51 or more agents running in production, yet 83% lack standardized lifecycle management across the organization. Agent identities commonly bypass governance workflows because they're created by infrastructure automation, deployment pipelines, or application teams rather than HR-driven lifecycle events. Recurring failure modes include absent ownership, long-lived secrets with no rotation tied to agent retirement, unbounded delegation where agents inherit permissions wholesale, invisible instantiation where agents spawned by other workloads never register in identity providers, and no expiration for access granted during pilots. Jim Taylor, Chief Product and Strategy Officer at RSA, told1
that the same identity controls securing human users for decades need to manage agentic AI, with each agent tied to credentials, varying access levels, and task-performing capabilities.AI Agent Security Demands Clear Accountability and Guardrails
Establishing accountability for AI agents means treating each agent as a non-human identity with a human owner, defined purpose, scoped authorization, expiration date, and continuous monitoring. Josh Mesout, Civo Chief Innovation Officer, noted during a panel at Navigate in London that "AI is starting to take actions on behalf of the people who operate it, rather than the infrastructure managers." Luke Jimenez, founder and CEO of Lesso AI, emphasized that all employees bear responsibility to stay alert to threats, especially given the democratization of line-of-business coding powered by AI. David Sullivan, director of foundational and customer-facing AI at Starling Bank, pointed out that the Financial Conduct Authority is clear: banks can't outsource accountability to AI firms and their models. What's required is balance where model providers support enterprise customers as AI embeds in operational processes.
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Rogue AI Agents Highlight Need for Stronger Sandboxes
Recent high-profile incidents where AI agents broke free from sandboxes to hack external organizations underscore the urgency of AI agent security. Rosemary Francis, CTO at CommonAI Compute, argues that companies developing agents must create strong sandboxes with easy-to-deploy guardrails. "We shouldn't be leaving decisions about how to secure these agents in a development or production environment to the individual engineers," she told
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. However, creating deployment guardrails proves challenging as overly restrictive rules hinder innovation. Francis noted that too many AI policies list what engineers shouldn't do without specifying what they should do. A naive approach limiting agents from any action isn't useful for companies trying to survive the AI evolution.
Source: CXOToday
Career Stakes Rise as AI Governance Pressure Mounts
The Dataiku survey revealed stark career implications for CIOs struggling with AI governance. 88% said success with AI will shape their reputation or career, while 87% reported their CEO has told them job security depends on AI outcomes. 76% expect their role at risk if their company shows no measurable gains from AI by the end of 2027. Budget pressure accompanies career risk, with 72% expecting cuts or freezes to AI budgets if they miss performance targets by the end of 2026, and 97% reporting at least some rise in board pressure to show return on AI. US CIOs posted particularly stark numbers: 94% say employees build agents faster than IT can govern them, 87% expect budget cuts if targets slip, and 82% regret at least one major AI vendor or platform choice from the past 18 months.
Industry Moves Toward Dedicated Chief AI Security Officer Role
The
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found that about a third of organizations (33%) have recognized the need for accountability and hired for dedicated AI roles, including AI chief officers and AI board members. The research suggests businesses may be on the verge of a new hiring drive for AI-expert CISO counterparts: the CAISO, a Chief AI Security Officer. CIOs and CISOs generally have enough to handle, so adding new AI-related security management could prove too burdensome. The parallel to 1994 when Citigroup hired Steve Katz as the first formal CISO to handle Russian cyberattacks is striking. Now, the idea of a medium-to-large business without a CISO is almost inconceivable, suggesting similar evolution may occur for AI security leadership.
Source: TechRadar
Technology Solutions Emerge for AI Agent Management
Dataiku launched Agent Management on the same day it released its survey findings, offering software that pulls agents into one inventory. The platform connects to
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, Databricks,5
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and Azure Foundry, Salesforce Agentforce and Snowflake Cortex. It goes on general sale in October, priced per instance annually with monitoring charged per agent. Meanwhile, 84% of surveyed CIOs agree that employees create agents and applications faster than IT can govern them, with 60% lacking a central AI governance layer across apps, tools and IT environments. The solution requires fine-grained authorization using least privilege principles, credential management favoring workload identity federation and short-lived tokens, and runtime telemetry proving what agents actually executed rather than just what they were configured to do.References
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