When Your AI Gets Compromised - We Respond.
Specialist AI incident response - 4-hour triage, ML forensics, root cause analysis, and post-incident hardening for prompt injection campaigns, model tampering, and agent exploitation.
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AI incident response is a specialist discipline that requires expertise at the intersection of security operations and machine learning systems. When a traditional security incident occurs, your IR team knows the playbook - isolate, investigate, remediate, recover. When an AI security incident occurs, the playbook is different: the attack surface is a model and its inputs, the forensics require understanding of LLM behavior and prompt manipulation, and the recovery may involve model retraining, permission redesign, or training data audit.
Why Traditional IR Fails for AI Incidents
Traditional incident responders are highly skilled at what they do. But AI security incidents present challenges that fall outside their training:
Investigation requires AI expertise. Reconstructing a prompt injection attack chain requires understanding how LLM context windows work, how system prompts interact with user inputs, and how indirect injection through external data sources operates. This is not general security knowledge.
Containment is different. You cannot “isolate” a compromised LLM the same way you isolate a compromised server. Model endpoints must be carefully managed - abrupt shutdown may create business disruption. Agent systems may need to be paused while their tool permissions are reviewed rather than simply terminated.
Impact assessment is novel. Determining what data was disclosed through model outputs requires reconstructing the adversarial prompt sequences and assessing what information the model was induced to reveal. This is forensic analysis specific to AI systems.
Recovery is not just restoration. Restoring from backup works for traditional systems. For AI systems, recovery may require model integrity verification, system prompt redesign, training data audit (if poisoning is suspected), and permission boundary redesign - none of which is in a standard IR runbook.
Retainer vs On-Demand
Retainer engagements give your organization guaranteed response SLAs, pre-established communication channels and escalation paths, a responder who already knows your AI architecture when an incident occurs, and quarterly preparedness reviews. For organizations with critical AI workloads in production, the retainer converts incident response from a reactive scramble into a prepared capability.
On-demand engagements are available for organizations that experience an AI security incident without a retainer in place. Response timelines are longer, but the full specialist capability is still deployed to investigate, contain, remediate, and harden.
Engagement Phases
Triage & Containment
Initial triage to confirm the AI security incident, scope assessment, emergency containment actions (agent halt, model endpoint isolation, API key rotation), and stakeholder notification. Incident commander assigned.
Investigation
Full forensic investigation: attack timeline reconstruction, prompt injection chain analysis, model behavior assessment, data exfiltration scope determination, access log review, and root cause identification.
Remediation
Vulnerability remediation - system prompt hardening, permission boundary enforcement, guardrail deployment, model integrity verification, and security control implementation to close the exploited attack vector.
Post-Incident Hardening
Comprehensive security hardening based on investigation findings, detection rule improvement, IR playbook development, and red team exercise recommendation for breach point validation.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Time to Triage | Unknown - no AI-specific IR capability | 4-hour triage SLA from engagement |
| Root Cause | AI incidents closed as 'unknown cause' | Full root cause analysis with attack chain reconstruction |
| Recurrence Prevention | Vulnerability remediated but detection gap remains | Hardening implemented + detection rules added to prevent recurrence |
Tools We Use
Frequently Asked Questions
What types of AI incidents do you respond to?
We respond to the full spectrum of AI security incidents: prompt injection campaigns (both direct and indirect), AI agent exploitation (where an agent's tool access is abused via prompt manipulation), model integrity incidents (evidence of model tampering or backdoor activation), data exfiltration via model outputs (sensitive data disclosed through adversarial prompting), training data poisoning (evidence that training data was manipulated), ML pipeline compromise (unauthorized access to training infrastructure or model artifacts), and model extraction attacks (systematic API abuse to steal model capabilities).
What is your response SLA?
For retainer clients, we guarantee engagement within 1 hour of incident notification and initial triage within 4 hours. For on-demand engagements without a retainer, we target initial engagement within 4 business hours and triage within 24 hours. For organizations with critical AI workloads, we strongly recommend a retainer to guarantee response SLAs for true AI emergencies.
Can you work alongside our existing incident response team?
Yes. AI incident response is most effective as a specialist overlay on your existing IR team: your team handles traditional IR scope (network, endpoint, identity), we handle AI-specific scope (model, agent, pipeline). We establish clear handoff points and communication protocols during onboarding so coordination is seamless during an actual incident.
What does post-incident hardening involve?
Post-incident hardening goes beyond fixing the specific vulnerability that was exploited. It includes comprehensive security review of all AI systems adjacent to the incident, deployment of monitoring to detect recurrence, detection rule development for the attack technique used, IR playbook documentation for this incident type, and optionally a targeted red team exercise to validate that the exploited attack vector is genuinely closed. We document everything so your team has an institutional record of the incident and the defensive improvements made.
What if we don't know we've been compromised?
Many AI security incidents are initially detected as functional anomalies - model behavior that seems different, unexpected outputs, agent actions that don't match business intent. If you observe anomalies that could indicate a security event but aren't certain, our triage engagement can assess whether a security incident has occurred. We treat potential incidents with the same urgency as confirmed ones until triage rules out a security cause.
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