Practice 02 · AI Governance
Saying no to AI is no longer realistic. Securing it end to end is. Agentic systems don't just extend what AI can do — they extend what an attacker can reach. We run a six-step programme that takes AI from ungoverned sprawl to an auditable, controlled estate.
Why now
AI reaches production faster than governance, review or control can be established around it.
MCP and multi-agent systems multiply attack vectors — agents call tools, pass context and act autonomously.
Shadow AI tools and unmonitored agent usage operate entirely outside the security function's view.
Risk spans code, models, agents and APIs at once. No single-layer control is sufficient.
Traditional tooling cannot detect AI-native or agent-driven threat behaviour.
DPDP, ISO/IEC 42001 and the NIST AI RMF are all moving from guidance toward expectation.
The programme
Each step is a delivery stage with its own controls, tooling and exit criteria — not a slide. We run them in sequence or join you at whichever stage you've reached.
You cannot govern what you cannot see. Step one is a complete, living inventory of every model, agent, notebook and MCP server in the estate — including the ones nobody told you about.
AI and agentic systems sprawl across cloud, MLOps and AgentOps platforms, usually without unified oversight. Misconfiguration and excessive permission are the norm, not the exception.
Models and agents can be manipulated through adversarial input, prompt injection and behavioural drift. Without structured testing, unsafe systems reach production and act autonomously.
Prompt injection, jailbreaking and tool misuse happen after deployment, where pre-production testing cannot reach. Agentic systems using MCP make independent calls and pass context between models.
PII, PHI and credentials surface in prompts, logs and outputs without anyone intending it. Multi-agent systems exchanging data dynamically multiply the exposure, and traditional DLP lacks the context to read AI-generated language.
Governance fails when it stays a policy document. Models, agents and datasets change continuously, and agents can act across systems or trigger external actions that quietly violate policy.
Threat model
Every category is mapped to OWASP LLM Top 10, OWASP Agentic Threats and MITRE ATLAS, so findings arrive in language your auditors and your board already accept.
LLM01, LLM07, LLM08 · T1, T2 · AML.T0051LLM03 · T13, T14 · AML.T0010LLM04 · T1, T6, T7 · AML.T0020LLM02, LLM07 · T12, T13 · AML.T0054LLM04, LLM05 · T1, T6, T7 · AML.T0080LLM06, LLM07 · T2 · AML.T0053LLM05, LLM08 · T12 · AML.T0080LLM06 · T6, T7 · AML.T0080LLM03 · T3 · AML.T0053LLM05 · T2, T12 · AML.T0051LLM04 · T11 · AML.T0069LLM08 · T12, T13 · AML.T0070Standards alignment
We don't invent a proprietary maturity model. Findings map to published frameworks so evidence transfers directly into audit, board reporting and regulatory response.
Engagement
A time-boxed inventory of models, agents and shadow AI, with an owner assigned to every asset found.
Posture review and red-team testing against the twelve categories, scored and prioritised.
Runtime controls, AI-native DLP and policy enforcement deployed into your environment.
Continuous monitoring, compliance correlation and audit-ready reporting run by our team.
Explore further
Most teams are surprised by their first AI inventory. A discovery sprint is the fastest way to see the real estate — and the fastest way to stop guessing.
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