AI incident response: what changes when AI is part of the attack

The two ways AI shows up in an incident

Most conversations about "AI and incident response" collapse two different problems into one. It helps to separate them before building or testing a plan.

The first is AI used by the attacker. This covers automated reconnaissance, phishing and business email compromise written well enough to pass as internal correspondence, deepfake voice or video used to authorise a payment or grant access, and AI-assisted vulnerability research and exploit development. The NCSC expects this last category, AI-assisted vulnerability research and exploit development, to be the most significant near-term AI-driven shift in the threat landscape. None of this changes what the incident is. A phishing-led compromise is still a phishing-led compromise. It changes how quickly it moves and how convincing it looks along the way.

The second is AI as part of the attack surface. This is newer and less well understood inside most incident response plans. It covers prompt injection against a customer-facing AI assistant, data poisoning of a model or its training data, an AI agent with more system access than it should have, a compromised AI integration or API key, an insecure vector store or RAG pipeline, and Shadow AI, staff using AI tools your IT team has never assessed. Here the AI system itself is the asset that gets compromised or misused, in the same way a server or an account would be in a conventional incident.

Our guides to agentic AI security risks and deepfake identity fraud cover how these specific attack types work in depth. This guide focuses on what they mean for the people running the response: what changes in detection, containment, investigation, and what to rehearse.

How AI changes detection and triage

Detection rules built to catch clumsy phishing do not catch AI-written phishing. The spelling mistakes, awkward phrasing and generic greetings that email filters and staff training have relied on for years are disappearing from attacker emails. That does not mean detection is hopeless. It means the signal has to shift from "does this look badly written" toward behavioural indicators: unusual sending patterns, mismatched reply-to addresses, requests that bypass normal approval steps regardless of how well they read.

Triage gets a new question. When an alert comes in, your team already asks what happened and how serious it is. Now they also need to ask whether AI was involved on the attacker's side, and separately, whether one of your own AI tools or agents is implicated. Those two answers lead to different playbooks. An AI-accelerated phishing incident is still a phishing incident: contain the account, reset credentials, check for lateral movement. A compromised AI agent or a prompt injection incident needs a different first move, because the "account" in question might be a service identity with broad API access and no obvious way to lock it out of a session the way you would a human user.

There is also a volume problem. AI agents and automated workflows now generate a meaningful share of normal traffic: scheduled API calls, automated data pulls, agent-to-agent requests. A SOC calibrated on human traffic patterns will either miss malicious automation hiding in that noise or drown in false positives every time legitimate automation looks unusual. Detection tuning has to account for what your own AI tooling does on a normal day before it can reliably flag what an attacker's AI tooling does on a bad one.

Containment and investigation get harder

Containment decisions assume you know what to switch off and who has the authority to do it. That assumption holds for a compromised laptop or a phished email account. It holds less well for a compromised AI agent wired into your CRM, your finance system and a customer-facing chat interface simultaneously. Revoking its credentials might be the right call. It might also break three production workflows at once. Deciding that in the middle of an incident, without having mapped what the agent touches in advance, costs time you do not have.

Investigation runs into a related problem: evidence quality. Traditional systems produce logs your incident responders know how to read: authentication events, file access, network traffic. Many AI tools and agent frameworks do not log prompts, outputs, and the actions an agent took with the same fidelity, or retain them for as long, as your existing SIEM does for conventional systems. Reconstructing what an AI agent actually did, and why it did it, can mean piecing together partial logs from several vendors rather than pulling one clean audit trail. Where you control the deployment, insist on comprehensive action logging as a condition of using the tool at all. Where you do not, such as a third-party AI feature embedded in a SaaS product, know that limitation before the incident, not during it.

There is also a supply-chain version of this problem. If a third-party AI provider embedded in one of your critical workflows suffers its own breach or outage, your containment options are limited to whatever your contract and their status page allow. Know in advance which workflows depend on an external AI vendor and what the manual fallback is if that vendor goes dark or is itself compromised, because that is not a decision you want to be making for the first time mid-incident.

Identity is now an incident-readiness problem

Machine identities, API keys, service accounts, OAuth tokens and now AI agent credentials, outnumber human identities by as much as 45:1 in a typical enterprise environment (Rubrik Zero Labs), and most organisations have inventoried only a fraction of them. Our machine identity security guide covers how that sprawl builds up and how to bring it under control. From an incident response standpoint, the relevant fact is simpler: if your plan only has a fast path for resetting a human employee's password, you do not have a fast path for the credential type most likely to be involved in an AI-related incident.

Credential theft remains the dominant way attackers get in regardless of AI. Stolen credentials are involved in over 80% of hacking-related breaches, and adversary-in-the-middle techniques that steal session tokens rather than passwords have made multi-factor authentication alone an incomplete defence. Our MFA bypass and credential compromise guide covers this in detail. AI changes the volume and credibility of the phishing and social engineering that leads to that initial credential theft. It does not change the fact that your playbook for revoking and rotating credentials, human and machine, is the thing that determines how fast you contain it.

When Shadow AI becomes an incident

Shadow AI, staff using AI tools your IT team has not assessed or approved, is usually discussed as a data governance problem. It is also an incident response problem, and it shows up in two different ways. The first is a standalone exposure event: an employee pastes customer data, source code or financial information into a personal AI account, and your organisation is now dealing with a data protection incident with no clear scope, because you do not know what else has gone through that channel or where it ended up. Our Shadow AI guide covers the governance and compliance side of this in full.

The second is subtler and more relevant here: Shadow AI shows up in the middle of an unrelated incident. A stressed engineer, trying to resolve an active outage or breach faster, pastes log data, error messages or a description of the compromise into an AI tool that was never approved for that purpose, in some cases the same tool the attacker is exploiting elsewhere in the estate. Your incident communications and evidence-handling procedures need to explicitly cover this: staff under pressure reach for whatever tool is fastest, and "do not paste incident data into unapproved AI tools" needs to be a stated instruction during activation, not an assumption.

A minute-by-minute AI-driven incident

A walkthrough makes the containment problem concrete. This scenario involves an AI agent with broad permissions, a credential leak, and a response that starts too slowly because nobody had mapped what the agent could touch.

1. 00:00, overnight: An API key belonging to an internal AI agent, one built to automate customer data lookups for the support team, is harvested from a misconfigured logging endpoint by an automated credential-scanning bot. No human notices, because nothing about the theft itself generates an alert.
2. 00:15: The stolen key is used to query the agent's API directly, bypassing the chat interface entirely. Because the agent's permissions were scoped to "whatever the support team might need" rather than the narrower set they actually use, the queries pull far more customer data than a support lookup ever would.
3. 00:45: Automated monitoring flags a volume spike on the API. The on-call engineer checks it, sees traffic that looks structurally similar to the agent's normal automated calls, and initially deprioritises it as a batch job running late.
4. 02:30: A second, larger spike triggers escalation. The security team now has to determine whether this is the agent behaving unusually or a compromised credential being used against the agent's API, a distinction the existing alert did not make and the team has not had to make before.
5. 03:10: Once compromise is confirmed, the team needs to revoke the agent's API key. Nobody has documented what breaks if they do. It takes another 20 minutes to establish that revocation will pause three customer-facing workflows, and to get the authority to accept that trade-off.
6. 03:45: Legal and privacy are brought in to scope how much customer data was actually exposed, working from partial API logs because the agent framework does not retain full request payloads beyond 24 hours.
7. 04:05: The agent's API key is revoked and the account disabled. The three customer-facing workflows that depended on it fail over to a manual process the support team last rehearsed at an earlier tabletop exercise, so the business keeps operating while the agent is offline.
8. 06:30: A replacement credential is issued with permissions scoped to what the support team actually uses, not the broad grant that enabled the original exposure, and the agent is brought back online restricted to that narrower access.
9. 09:00: Support operations return to the automated workflow under the new permissions. The manual fallback stands down, and the incident moves from containment into the investigation and notification work the earlier steps set in motion.

Every one of those delays traces back to a gap that existed before the incident started: no inventory of what the agent could access, no pre-agreed authority to revoke its credentials, no alerting that distinguished the agent's normal automated behaviour from abuse of it, and logging that did not retain enough detail for a clean investigation. None of that is a new discipline. It is the same incident readiness work your organisation should already be doing, extended to cover an asset type most plans currently ignore.

Tabletop scenarios worth rehearsing

Most tabletop exercises still run ransomware, phishing and data breach scenarios written before AI-specific incidents were common. Those remain worth rehearsing, but they will not test whether your team can handle the scenarios below.

1. Prompt injection through a customer-facing AI assistant: a crafted document or message manipulates your chatbot or AI assistant into revealing internal data or taking an unauthorised action. Who is notified, and who has authority to take the assistant offline?
2. A deepfake voice or video approval request: a convincing cloned voice or fabricated video call requests an urgent wire transfer or access grant. What verification step exists that does not rely on recognising the voice or face, and does everyone involved actually know it?
3. A compromised, over-permissioned AI agent: a leaked credential is used against an internal AI agent's API, as in the scenario above. Who can revoke its access, and what breaks when they do?
4. Shadow AI use during an active incident: a staff member, trying to resolve the incident faster, pastes sensitive incident data into an unapproved AI tool. Does your plan say anything about this, and does the comms team know to say it out loud during activation?

Run at least one of these alongside your existing scenarios at the next exercise. The goal is not a perfect answer. It is finding out where the gap is while it still costs nothing to find.

Readiness checklist

None of this replaces the fundamentals covered in our incident response plan guide. It extends them to cover the asset types and attack paths that plan of even a year or two ago was not written to account for.

How Cyvra helps

Cyvra's Cyber Incident Readiness service builds and rehearses incident response capability that accounts for the full range of triggers a business faces today, cyber and AI-driven, not just the scenarios a plan was originally written around.

• AI asset inventory: map the AI agents, integrations and machine credentials in your environment, what each can access, and who can revoke it
• AI-specific incident playbooks: containment and investigation procedures for compromised AI agents, prompt injection and Shadow AI exposure events
• Deepfake verification procedures: practical out-of-band verification steps for high-value approvals that do not rely on recognising a voice or face
• Tabletop exercises with AI-specific scenarios: rehearsed alongside your existing ransomware and data breach playbooks, not as a separate exercise nobody attends
• Credential and machine identity readiness: pre-agreed authority and process for revoking API keys, service accounts and AI agent credentials during an incident

Contact our Cyber Incident Readiness team or our AI practice to start with an Incident Readiness Assessment.

For the announcement of this service and the broader threat landscape it was built for, see our post on introducing Cyber Incident Readiness.