The first time your team handles an AI incident should not be during one.
If an AI system leaked data, used the wrong tool, or took an unsafe action tomorrow, would your team know exactly who responds, what gets escalated, and how the incident is handled?
You should look at this if
You have AI in production (agents, copilots, customer-facing) and no practised incident response for it.
Your IR playbook mentions "LLM" or "agent" zero times.
You can't confidently name who owns the first hour of an AI-specific incident.
Duration
1 to 2 weeks prep plus a facilitated half-day workshop with legal, comms, security, and engineering.
Deliverable
After-action report with gap scoring; dated action register; named owners per gap.
Framework mapping
NIST AI RMF, OWASP LLM Top 10 scenarios, and existing IR playbook fragments.
Board, production, incident, or scale: the trigger is rarely curiosity. Each pressure maps to a primary service we'd start with. Click the code on the right to jump to that brief.
Board pressure
You need a defensible answer.
The board, customers, audit, or regulators will ask how AI is governed. A policy deck is not enough. You need evidence, ownership, and a visible plan.
Start with
Production pressure
Your AI is live, but not properly tested.
Traditional security testing can say the app is clean while the agent still has dangerous paths, unsafe tool access, or prompt-driven behaviour nobody has validated.
Start with
Incident pressure
Your team has never handled an AI incident.
When ownership is unclear, the first hour is expensive. You need to pressure-test the decision chain before legal, comms, security, and engineering are improvising.
Start with
Scale pressure
You are spending on AI without knowing what should scale.
Pilots multiply, spend rises, and leadership still cannot tell where the next budget pound or dollar should go. You need a maturity view tied to action.
Start with
Most buyers don't need every service. They need the right first move.
Four common starting paths. Each pairs an opening engagement with the follow-through that tends to come next.
Visibility path
We do not know where AI is being used or how exposed we are.
Start with posture, then test incident readiness, then keep the roadmap alive.
AISPAAI-TTXRetainer
Readiness path
The board wants to know if we could handle an AI incident.
Start with the exercise, fix the chain, then baseline the rest of the posture.
AI-TTXAISPARetainer
Technical path
We have production AI and need adversarial validation.
Scope the exposure, red-team the system, then harden the control path.
Focused scopingAI-RTAHardening plan
Scale path
We need to know whether AI adoption is mature enough to scale.
Start with maturity, go deeper on security where needed, then execute against the roadmap.
AIMAAISPAExecution support
What you walk away with
Every engagement hands back the same four artefacts, tuned to the service.
01
Board-ready narrative
A concise answer leadership can use: what is happening, what is exposed, what matters most, and what should happen next.
02
Evidence register
Every major point traceable back to interviews, artefacts, configurations, telemetry, or tested behaviour.
03
Prioritised action register
Not generic recommendations. A sequenced view of what gets fixed first, what waits, and why, with named owners and dates.
04
Framework-aligned structure
NIST AI RMF, OWASP, MITRE ATLAS, and ISO/IEC 42001 readiness concepts. Drops into your existing GRC workflow.
Who leads the work & framework alignment.
Credibility is specificity. Here's who's doing the work and the frameworks every engagement maps to.
Mukund Hirani
Founder, RAXE AI Security
Mukund has worked across national security, incident response, threat intelligence, and enterprise security environments, including GCHQ, Mandiant, FireEye, and CrowdStrike. That shapes RAXE advisory work: evidence-led, operationally grounded, and focused on the decisions leaders need to defend.
GCHQMandiantFireEyeCrowdStrike
Framework alignment
Findings, scorecards, and action registers drop straight into existing GRC workflows, not in parallel to them.
NIST
AI RMF
OWASP
LLM · ML Top 10
MITRE
ATLAS
ISO 42001
AI management
Next step
Scope your engagement in 30 minutes.
You leave with three things: a clear first move, a success definition you can share with leadership, and a timeline you can plan against.