AI Agent Authorisation Review
Make delegated authority inspectable. Trace who an agent acts for, what it can access and how that access can be withdrawn.
AI Security and Privacy
Know what an agent can reach, and what it can reveal.
Examine agent authority, adversarial behaviour, isolation boundaries and personal-data flows in an agreed threat and processing context.
Book a 30 minute readiness callKnow what an agent can reach, and what it can reveal.
- Delegated authority
- Review agent identities, permissions and authorization at the point of consequential action.
- Adversarial behaviour
- Assess misuse, sandbox boundaries and deployment controls in an agreed threat context.
- Personal data
- Examine data flows, access and retention across AI components and external providers.
Explore the engagement scope
Examine agent authority, adversarial behaviour, isolation boundaries and personal-data flows in an agreed threat and processing context. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.
AI Agent Runtime Security Review
Make every delegation explicit. Examine who the agent acts for, where it can execute and which tools it can reach when instructions alone are not enough.
AI Agent Red Team
Challenge the boundaries that matter to your workflow. Test agreed attack scenarios against agent instructions, tools and data access.
AI Agent Sandbox Assurance
Examine the boundary between agent execution and the systems around it, including files, networks, secrets and cleanup.
AI Secure Agent Architecture Review
Design authority and isolation into the system. Review trust boundaries, credentials, tools and recovery before deployment choices become difficult to change.
AI Secure Agent Deployment Review
Look beyond a successful demonstration. Examine the permissions, dependencies and operational controls that shape an agent’s behaviour in deployment.
AI Privacy Engineering Review
Trace personal data through your agent workflow. Examine collection, memory, retrieval, tool access and retention against your agreed requirements.
Supporting capabilities
Agent security and cybersecurity
Examine how adversarial inputs, tools and dependencies can change agent behaviour.
Agent identity, authorisation, and delegation
Define who an agent acts for, which permissions it receives and how authority ends.
Agent sandbox assurance
Examine execution isolation, network access, secrets and recovery boundaries.
AI governance and regulatory readiness
Connect inventory, accountability, oversight and evidence to AI operating decisions.
Privacy engineering for AI systems
Examine data flows, retention, access boundaries and privacy requirements in context.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What does AI Security and Privacy cover?
Examine agent authority, adversarial behaviour, isolation boundaries and personal-data flows in an agreed threat and processing context. Indicative focus areas include agent identity and authorisation, threat modelling, adversarial testing, sandbox boundaries, deployment controls, data flows and retention.
Why are security, identity and privacy one family?
They answer one question from three sides: what an agent may act as, how it can be misused, and what it can expose. In a deployment those are the same boundary, and separating them tended to split a single review across three engagements. Each discipline keeps its own capability page, and an engagement is scoped to the question you actually have.
We have a data-protection question rather than a security one. Is this the right family?
Yes. Privacy engineering sits here because the same boundary decides what personal data an agent can read, retain and send onward. The family covers privacy review work scoped for privacy and data protection owners, and the privacy discipline has its own capability page linked from this one. The work examines technical and operating controls; it does not determine lawful processing, approve transfers, certify compliance or provide legal advice.
What is needed to scope AI Security and Privacy?
Examine agent authority, adversarial behaviour, isolation boundaries and personal-data flows in an agreed threat and processing context. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.
What decision does AI Security and Privacy support?
Know what an agent can reach, and what it can reveal. Findings and indicative outputs apply to the agreed scope. Your accountable owner retains deployment, investment and risk decisions.
What can this agent reach, and what can it expose?
Bring the workflow, the constraints and the evidence you already have.