TL;DR
AI agents create a new class of security incidents because they can take actions, not just generate outputs.
More than 120 organizations are now backing the Open Secure AI Alliance, which is working with the Linux Foundation on the Shared AI Findings Exchange, or SAFE, for documenting and sharing agentic AI security incidents.
SAFE is designed to create a common language for incidents such as unauthorized access, exposure of confidential information, and actions taken after an agent recognizes it may be acting outside its authority.
The bigger issue isn’t incident reporting itself. It’s that traditional security incident models weren’t built around autonomous systems operating with delegated authority.
I think AI incident response will eventually become its own discipline, combining cybersecurity, identity, governance, and agent behavior monitoring.
AI Agents Are Creating a New Kind of Incident
For most of cybersecurity history, an incident has been relatively easy to describe. Someone stole credentials. Malware entered the environment. A vulnerability was exploited. Data was exfiltrated. Even when the attack was complicated, there was usually a recognizable human or technical mechanism behind it.
AI agents complicate that model because an incident can happen through legitimate access. An agent might have valid credentials, access approved tools, and still perform an action that nobody intended. It could follow a malicious instruction embedded in data, misinterpret its objective, delegate work to another agent, or continue operating after it should have stopped. Microsoft has described agents as systems that operate with delegated authority and can affect multiple business systems at once, which is exactly what makes these failures different from conventional application incidents.
That distinction matters because the question changes. Instead of only asking, “How did the attacker get in?” security teams may have to ask, “Why did the agent believe this action was authorized, what instructions influenced it, who gave it the authority, and how far did that authority travel?”
The Industry Is Starting to Build a Vocabulary for These Failures
That’s why I found the SAFE initiative interesting. More than 120 organizations in the Open Secure AI Alliance are working on proposed guidelines with the Linux Foundation for documenting and sharing agentic AI cybersecurity incidents. NVIDIA, Cisco, CrowdStrike, Hugging Face, and Red Hat are among the organizations contributing to the effort.
The goal is bigger than creating another incident-reporting form. SAFE is an attempt to establish a shared structure for describing what happened when an AI agent behaves unsafely or crosses an authorization boundary. The proposed framework covers events such as unauthorized access to third-party systems, confidential information exposure, and actions taken after an agent recognizes that it may be acting without proper authorization.
I think that’s an important milestone because security improves when incidents become comparable. If one organization calls an event “agent misbehavior,” another calls it “automation failure,” and a third treats it like a standard credential incident, it’s difficult to build collective intelligence. A common vocabulary makes it easier to identify patterns, compare controls, share mitigations, and learn from failures across the industry.
The Hard Part Is Accountability
But there’s a much harder question underneath all of this: who is actually responsible when an AI agent goes rogue?
Imagine an agent operating a finance workflow. It has a legitimate identity, was approved by the business, and has access to the systems it needs. A malicious instruction enters through a document. The agent follows it, initiates an unauthorized action, and causes financial loss. Who owns the incident? The employee who deployed the agent? The team that built it? The security team? The model provider? The person who supplied the document? Or the organization that gave the agent its permissions?
Traditional incident response has some assumptions built around human accountability. AI agents blur those boundaries because the system taking the action may be neither a person nor a traditional software process. CoSAI has already identified this as an identity and access problem, arguing that agents need distinct, verifiable identities, short-lived credentials, traceable chains of authority, and immutable records of who authorized what.
That suggests AI incident response will eventually need to capture much more than timestamps and IP addresses. It may need the agent’s identity, owner, task, permissions, instructions, delegated authority, tool calls, memory influences, and the sequence of decisions that led to the incident.
Incident Response Will Have to Change
I don’t think enterprises can simply bolt an “AI incident” checkbox onto their existing incident response platforms and call it done.
AI incidents often happen through authorized channels. An agent can use legitimate credentials and approved APIs while still producing an unauthorized outcome. A traditional incident response workflow may tell you exactly which account made an API call, but not why the agent made that call, what it was trying to accomplish, or whether the action was part of a larger sequence that only became dangerous when viewed as a whole.
That’s why I expect AI incident response to evolve toward much richer behavioral evidence. Security teams will need to reconstruct agent trajectories, trace chains of authority, understand delegation between agents, and determine where the system first diverged from its intended objective. The goal won’t simply be to stop the incident. It will be to understand the agent well enough to prevent the same class of failure from happening again.
My Perspective
I believe the most important thing about SAFE isn’t the framework itself. It’s what its existence tells us.
The industry is starting to recognize that autonomous AI systems are going to fail in ways we haven’t had to document before. And when something is new, the first step toward making it safer is learning how to describe it consistently.
We already have common languages for vulnerabilities, malware, cloud incidents, and software weaknesses. AI agents are now creating enough new failure modes that they need one too.
A few years from now, I suspect “AI incident response” will be as normal a discipline as application security or cloud security. Organizations will have dedicated playbooks for agent failures, identity compromise, unsafe delegation, memory poisoning, and unauthorized autonomous actions.
The question isn’t whether AI agents will eventually go wrong. They will. The real question is whether we’ll be able to understand why, assign responsibility, contain the damage, and make sure the next agent doesn’t make the same mistake.
Prompt of the Day
Act as an AI incident response lead. An autonomous AI agent has taken an action that may have exceeded its intended authority. Reconstruct the incident by identifying the agent’s identity and owner, original objective, permissions, instructions it received, tools and data it accessed, any delegated actions, the full sequence of actions, and the point where its behavior diverged from policy. Then classify the incident, identify the likely root cause, assess the potential blast radius, recommend immediate containment steps, and propose controls that would prevent the same failure from recurring.