AI Incident Response: Extending Your Existing IR Plan

VERITY + SENTRY — Incident Response

AI Incident Response: Extending Your Existing IR Plan

AI incident response is the disciplined extension of your existing security incident response plan — covering the new attack classes, evidence types, regulatory triggers, and remediation paths AI systems introduce, without rebuilding the program from scratch.

By Dale Boehm, CEO Armorstack · CISA, CDPP
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Last reviewed: May 1, 2026
What AI Incident Response Is, In One Sentence

AI incident response is the disciplined extension of your existing security incident response plan to cover the new attack classes, evidence types, regulatory triggers, and remediation paths that AI systems introduce — without rebuilding the program from scratch.

The thesis matters. Most mid-market organizations do not need a separate AI IR program. They need their existing IR plan extended in five specific places. This guide covers those five places.

Why It Matters

Why It Matters More Than Most Teams Realize

AI incidents are happening today in mid-market environments. Most are not yet recognized as incidents because the existing IR program was not designed to detect them, classify them, or escalate them. Three reasons this warrants priority attention:

01

The Detection Telemetry Is Different

Traditional IR pulls from EDR, SIEM, firewall, DNS, identity, and DLP. AI incidents pull from prompt logs, model output logs, retrieval logs, agent action logs, and tool-call logs — telemetry sources most mid-market SIEMs do not yet ingest.

02

The Regulatory Clock Starts In Different Places

A model inversion event that leaks training data may trigger HIPAA, GLBA, GDPR, and state breach notification at the same time, with different clocks and different evidentiary requirements. Under the EU AI Act, serious incident reporting for high-risk systems runs as tight as 2–15 days.

03

The Remediation Toolkit Is Different

Reverting a poisoned model is not the same as quarantining an endpoint or rotating credentials. Rolling back model-initiated actions — sent emails, executed transactions, modified records — requires capabilities most IR teams have never exercised.

Incident Taxonomy

The Seven AI Incident Classes

A practical incident classification that your IR runbook should explicitly cover. Each class has a distinct detection signal, a distinct evidence type, a distinct remediation path, and a distinct regulatory implication.

1

Prompt Injection Compromise

The model has been induced to act on adversarial instructions. Indicators include unusual tool-call patterns, output content that does not match intended assistant behavior, and downstream system effects — emails sent, records modified, data accessed — attributable to model-initiated actions. Coverage: Prompt Injection Prevention →

2

Inference Exfiltration

The model has disclosed sensitive content — system prompt, retrieved documents, multi-tenant cross-bleed, tool output — to a user who should not have received it. Coverage: Inference Exfiltration →

3

Model Inversion or Membership Inference

A pattern of queries has reconstructed training data or inferred membership of specific records in the training set. Detection signals include query-volume bursts, near-duplicate prompts, and confidence-probing patterns. Coverage: Model Inversion Attacks →

4

Data Poisoning Detection

A model in production exhibits behavior consistent with a backdoor trigger or trained-in misclassification. Detection often surfaces via behavioral testing, user reports, or downstream system anomalies. Coverage: Data Poisoning →

5

Shadow AI Exposure Event

An unsanctioned AI tool has been confirmed to be processing sensitive corporate data — pasted into a public LLM, sent through a vendor’s AI feature, processed by an unauthorized agent. Coverage: Shadow AI Detection →

6

AI-Augmented Social Engineering

An attacker has used AI capabilities — generated phishing, voice clones, deepfake video, automated reconnaissance — against the organization. The AI is the attacker’s tool, not the victim, but the response is still an AI incident response.

7

AI Vendor Incident

A SaaS vendor with AI features active in the environment has disclosed an incident affecting their AI layer — model compromise, training-data breach, multi-tenant cross-bleed. The organization is downstream affected.

The Extension Model

The Five Extensions to Existing IR Plans

These are the five additions that extend a competent traditional IR plan into AI coverage. Build them as updates to existing playbooks, not as a parallel program.

01

Detection — Extend the Telemetry Stack

Add four log sources to the SIEM:

  • Prompt and response logs for every LLM-touching system, with metadata (user, session, tool calls, retrieved documents)
  • Retrieval logs from RAG pipelines, including the requesting user identity, the query, the documents returned
  • Agent action logs for every agent — tool calls, arguments, outputs, downstream effects
  • AI provider egress for shadow AI detection — DNS, TLS, CASB telemetry on AI provider domains

Add behavioral baselines:

  • Normal output length and content patterns by assistant
  • Normal query-volume patterns by user and tenant
  • Normal tool-call frequency and arguments

Add detection rules:

  • Prompt-injection signature matching on inputs
  • Sensitive-content exfiltration signatures on outputs
  • Anomaly alerts on behavioral baselines
  • Shadow AI activity outside the sanctioned allowlist
02

Triage — Extend Incident Classification

Update the incident classification matrix:

  • Add the seven AI incident classes above
  • Map each class to severity by data sensitivity, blast radius, and regulatory exposure
  • Define the on-call escalation path — most mid-market firms route AI incidents to the same SOC analyst as traditional incidents, with an AI-savvy escalation contact for technical depth

Update the initial-response checklist:

  • For prompt injection: identify affected agents, suspend agent action capabilities, preserve session context
  • For inference exfiltration: identify what was disclosed, identify recipients, preserve full prompt-response logs
  • For model inversion: capture query patterns, suspend the affected model API, preserve query logs
  • For data poisoning: identify the suspected trigger pattern, freeze the training pipeline, preserve datasets and model checkpoints
  • For shadow AI: identify the data exposed, the user(s) involved, the vendor; engage user-conduct and HR processes as needed
03

Forensics — Extend Evidence Preservation

AI incidents require evidence types most IR programs are not yet collecting:

  • Model artifacts — weights, configuration, system prompts at the time of incident
  • Training artifacts — datasets, training logs, hyperparameters for poisoning incidents
  • Conversation context — full prompt history including system prompt, retrieved content, tool calls, responses
  • Model behavior snapshots — for memorization and inversion incidents, controlled-prompt outputs that document the leak
  • Retrieval index state — for RAG incidents, the index version and document permissions at the time

Establish chain-of-custody procedures for these artifacts equivalent to your existing endpoint and log evidence procedures. The legal and regulatory standards are the same.

04

Remediation — Extend the Response Toolkit

AI-specific remediation capabilities your IR team needs to be able to execute:

  • Roll back agent actions — revoke sent emails, reverse database modifications, retract API calls
  • Suspend model APIs — kill switch on a per-assistant basis without taking down the broader application
  • Rotate compromised system prompts and embedded credentials
  • Restore from pre-poisoning model checkpoints — requires you to keep them, which most mid-market AI teams do not by default
  • Quarantine retrieval indexes — for RAG cross-permission events, isolate the affected index until permissioning is corrected
  • Coordinate with AI provider — for incidents involving foundation-model providers, the disclosure and incident-coordination workflow
05

Regulatory and Notification — Extend the Reporting Matrix

The regulatory map for AI incidents is broader than for traditional incidents:

  • HIPAA — for healthcare data breach via model inversion, inference exfiltration, or shadow AI exposure
  • GLBA, state breach laws — for financial-services and PII breach
  • GDPR — for personal data of EU residents disclosed via AI
  • EU AI Act — for serious incidents in high-risk AI systems and GPAI systems with systemic risk; tight reporting deadlines
  • Sector-specific — OCC SR 11-7 model risk events, FTC Section 5 deception risks (for hallucination-induced consumer harm), SEC for material AI incidents at public companies
  • Contractual — many enterprise customer contracts now include AI-specific incident notification clauses; review your contractual matrix annually

Update the notification decision tree to reflect each. The decision in the first 24 hours of an incident usually determines whether you meet your tightest reporting deadline.

Exercise Program

Tabletop Scenarios for AI Incidents

A practical tabletop schedule. Run these with the actual on-call analyst, not just the IR team lead — the tabletop is where the runbook gaps become visible.

Quarter 1

Prompt injection compromise of a customer-facing assistant — what does the SOC see, who declares the incident, who suspends the agent, who notifies customers.

Quarter 2

Inference exfiltration of confidential documents through a RAG misconfiguration — full disclosure analysis, regulatory clock determination.

Quarter 3

Suspected data poisoning detected post-deployment — pipeline freeze, lineage trace, rollback decision.

Quarter 4

Shadow AI event — sensitive document pasted into a public LLM by an employee — HR coordination, vendor outreach, user awareness response.

Our Approach

How Armorstack Approaches AI Incident Response

When we onboard a client, we run the AI IR work via the VERITY portfolio for design and the SENTRY portfolio for execution:

1

Existing IR Plan Review

What’s there today, what attack classes are covered, what the SOC sees and does not see.

2

Telemetry Gap Closure

Add prompt logs, retrieval logs, agent action logs, and AI egress to the SIEM.

3

Runbook Extension

Update incident classification, triage, forensics, remediation, and notification per the five extensions above.

4

Tabletop Rotation

Quarterly exercises through the four scenarios above, then iteration.

5

24×7 Detection

SENTRY’s AI security observability operating as the production detection layer, with Armorstack analysts as the escalation path.

Most mid-market clients have meaningful gaps in telemetry and remediation tooling on the day we start. Closing those two areas typically defines the first 90 days.

FAQ

Common Questions

Do we need a separate AI IR plan, or is this an extension of the existing plan?
Extension, in almost every mid-market case. A separate plan creates handoff seams between the AI on-call and the traditional on-call, which is exactly what an attacker wants. Build into the existing plan.
What’s the smallest first step?
A telemetry gap analysis. Pull what your SIEM ingests today, compare against the four AI log sources above, produce a gap list with effort estimates. This is usually a 3-5 day exercise that scopes the rest of the program.
Who owns AI incident response — the CISO, the CIO, the data team, or someone new?
The CISO, in almost every case. AI IR is security incident response with a new attack surface. Splitting it off into a separate function produces coordination problems and slower response times. The technical depth comes through specialized advisors and AI-aware SOC analysts, not through a parallel reporting line.
How does this interact with SR 11-7 model risk management for financial-services firms?
SR 11-7 governs model performance, validation, and ongoing monitoring. AI security incident response governs adversarial events. They are complementary — a poisoned model is both a model-risk event and a security incident — but they have different owners (model risk vs. CISO) and different remediation paths. A coordination matrix between the two functions is essential.
What about user awareness for AI incidents?
Three additions to your existing security awareness program: (1) what shadow AI looks like and how to report it; (2) how to recognize AI-augmented phishing, voice clones, and deepfake attempts; (3) what to do when an AI assistant produces unexpected behavior — report it, do not act on it. Existing awareness platforms can absorb this content; it does not require a new program.
What does Armorstack’s offering look like?
VERITY runs the runbook extension and tabletop work as a structured advisory engagement. SENTRY provides 24×7 AI-aware detection, an IR retainer, and the telemetry infrastructure to feed it. Most engagements begin with a Shadow AI Discovery and an IR Plan Extension Assessment.

Is Your AI Incident Response Posture Theoretical?

If your existing IR plan does not explicitly cover the seven AI incident classes above — and your SIEM does not ingest prompt logs, retrieval logs, or agent action logs — the honest answer is yes. Book a 30-minute discovery call. Book a Discovery Call →

Prefer to talk to a person? Call 877-890-5508 or email [email protected].

Last reviewed: May 1, 2026 · Authored by Dale Boehm, CEO Armorstack · CISA, CDPP