AI & Agent Assurance
Agentic AI Security Assessment & Red Teaming
We attack your agents the way a motivated adversary would — through the documents they read, the tools they call and the permissions they hold — and show you exactly where a single poisoned input becomes a business-impacting action.
The challenge
An agent is only as safe as the least trustworthy thing it reads.
Traditional application testing assumes code does what it was written to do. Agents do what they are persuaded to do. Instructions arrive through emails, web pages, tickets and retrieved documents; the agent then chooses which tools to call and with what arguments.
That turns familiar weaknesses into new attack paths. Over-privileged tool access becomes lateral movement. Missing approval steps become unauthorised payments or data sharing. Thin logging makes it impossible to reconstruct what happened, or to prove to a regulator that it did not.
We test agentic systems end to end, and we report in the language of your control framework — so the output is a fix list your engineers can act on and an assurance statement your board can rely on.
What you get
Outcomes, not reports.
- 01
A realistic threat model
How your agent could be manipulated, by whom, and with what consequence — grounded in your architecture, not a generic checklist.
- 02
Proven attack paths
Demonstrated chains from untrusted input to unauthorised action, with the evidence your engineers need to reproduce and fix them.
- 03
Tollgate-by-tollgate findings
Every weakness mapped to Authorisation, Execution, Observation or Containment, so fixes land on the right control.
- 04
Retest and assurance
Verification that remediations work, and a statement of assurance you can share with stakeholders.
How we work
Senior-led from scoping to sign-off.
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Model
We map the agent’s goals, tools, data sources, identities and trust boundaries, and agree rules of engagement and safety limits.
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Attack
Direct and indirect prompt injection, tool and function-call abuse, excessive agency, privilege escalation, memory and context poisoning, and exfiltration via outputs and side channels.
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Test the controls
We check whether guardrails, policy checks, human approvals, logging and kill switches actually engage when they should.
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Report and retest
Findings with severity, reproduction steps and fixes, followed by verification once remediation is complete.
Deliverables
- Agent threat model and attack surface map
- Technical findings with reproduction evidence
- Tollgate control-effectiveness assessment
- Executive summary and risk statement
- Retest report
Who it’s for
- Teams preparing to put an agent or copilot into production with access to real systems or data
- CISOs who need independent assurance before signing off an agentic use case
- Product and platform leaders building agent frameworks for internal reuse
- Firms whose regulators or customers expect evidence of AI-specific testing
Aligned to
- OWASP GenAI Security Project guidance
- MITRE ATLAS
- NIST AI RMF & AI 600-1 (Generative AI profile)
- UK Code of Practice for the Cyber Security of AI
- ISO/IEC 42001
Questions
Frequently asked.
Is testing safe to run against production?
We agree rules of engagement, safety limits and, where possible, test against representative non-production environments. Anything touching production is scoped, supervised and reversible.
Do you test the model or the application?
Primarily the application and agent layer — tools, permissions, data flows and controls — because that is where most business-impacting risk sits. Model-level behaviour is tested insofar as it affects those controls.
Can this support a regulatory or customer assurance request?
Yes. Reporting can be structured to support internal audit, regulators and customer due diligence, with a shareable summary separate from the technical detail.
Often paired with
AI Agent Access Review
An independent review of every agent, service account and token acting on your behalf — who owns it, what it can reach, and whether it should.
Explore AI Agent Access ReviewAI-Incident Tabletop Exercises
Executive and operational exercises built around a rogue or compromised agent. Find the gaps before an incident — or a regulator — does.
Explore AI-Incident Tabletop ExercisesAI Governance, ISO/IEC 42001 & EU AI Act Readiness
An AI management system your auditors, regulators and board can follow, mapped to ISO/IEC 42001 and the EU AI Act.
Explore AI Governance, ISO/IEC 42001 & EU AI Act ReadinessStart with a confidential conversation.
A confidential conversation about Agentic AI Security Assessment & Red Teaming with the senior practitioner who would lead the work. No sales team, no obligation.