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AI security assessment and red teaming

Test LLM applications and AI agents for prompt injection, data leakage, excessive agency and unsafe tool use before they go wide.

Typical timing
3–6 weeks
Engagement
Fixed scope
Delivery framework
User researchDiscoveryAlphaBetaLive

Assess the security of AI systems you build or adopt — chatbots, copilots, retrieval systems and autonomous agents — against the OWASP Top 10 for LLM Applications and agentic threat guidance, and fix what could leak data or let an attacker steer the system.

  • An AI assistant or agent is about to be exposed to customers or staff
  • Agents have access to email, files, payments or production systems
  • Security teams have no test method for AI
How it runs

Activities, step by step

The plan follows our delivery framework. Steps that do not apply to this kind of work are left out rather than padded.

  1. 02 · Discovery1 week

    Threat model

    • Architecture, data flows, tools and permissions mapped
    • Threat model against OWASP LLM Top 10 and MITRE ATLAS
    • Test plan and safe-testing boundaries agreed
  2. 03 · Alpha1–3 weeks

    Adversarial testing

    • Direct and indirect prompt injection
    • Data leakage, jailbreak and system prompt extraction
    • Tool misuse, excessive agency and privilege escalation
  3. 04 · Beta1–2 weeks

    Harden

    • Findings reported with fixes
    • Guardrails, permission scoping and human approval points designed
    • Retest of fixes
  4. 05 · LivePer release

    Keep testing

    • Adversarial test suite added to the evaluation pipeline
    • Retesting on model or prompt changes

Deliverables

What you keep at the end.

  • AI threat model
  • Adversarial test report with rated findings
  • Guardrail and permission recommendations
  • Reusable adversarial test suite

Outcomes

What it is built to change.

  • AI launched without avoidable data leakage or hijacking risk
  • Agents with the least privilege they need
  • Evidence for risk, compliance and customers