Secure enterprise AI systems and establish risk-based AI governance across models, data, retrieval, connected tools, autonomous agents and business workflows.
Evaluate the full AI system—from data and models to retrieval, tool access, permissions and autonomous actions.
Typically for: CISO, CIO, AI leader, data governance, product and model-risk functions
Select a focused assessment or combine specialist modules under a defined statement of work.
Inventory, accountability, use-case risk, oversight and policy controls.
Discuss this scopeTest retrieval boundaries, prompt injection, sensitive data and connected APIs.
Discuss this scopeReview tool privileges, autonomous decisions, human approvals and misuse scenarios.
Discuss this scopeExplore the modules relevant to your environment. Each module is selected against objectives and competencies confirmed during scoping.
AI use-case inventory, materiality, risk ownership, lifecycle governance, policy, oversight and control design.
Model hosting, access rights, data flows, API boundaries, inference security and third-party dependencies.
Prompt injection, instruction manipulation, output misuse, sensitive information disclosure and misuse cases.
Document access segregation, retrieval permissions, vector stores, poisoning risks and data leakage.
Tool permissions, agent authority, approval gates, credentials, execution limits, logging and autonomous workflows.
Controlled red-team scenarios, business-specific abuse cases, attack paths and technical remediation evidence.
External models, agent frameworks, connected tools, hosting and supplier control boundaries.
NIST AI RMF, ISO/IEC 42001, OWASP GenAI and applicable financial-sector AI guidance.
AI governance and privacy risk requirements across enterprise use, data handling, agent permissions, risk oversight, AI assessments and applicable regional AI frameworks, including Singapore guidance.
Defined scope and evidence standards make the specialist work useful to both technical owners and senior management.
Identify AI use cases, data, prompts, identities, models, agents and tool access.
Define misuse, adversarial input, excessive permissions and control requirements.
Validate representative risks using scoped controlled scenarios.
Prioritise fixes, ownership and readiness decisions for security and governance.
Illustrative samples use fictional data. Actual deliverables and report depth are defined by your engagement scope.
See how technical evidence is connected to exploitability, business impact and corrective actions.
Explore example ILLUSTRATIVE OUTPUT · GRC & AUDITTechnology risk & ITGC controls matrixExplore an evidence-linked control assessment connecting risk, control, testing and remediation.
Explore examplePrevious specialist experience helps inform our assessment approach to the relevant technical, regulatory and business risks.
Regional CISO and enterprise security architecture experience in a multinational industrial environment, including IT/OT security, cloud risk and governance assessments of enterprise AI and agentic workflows.
Cybersecurity consulting experience in a Singapore government environment involving ICT risk assessment, security architecture, policy review, application security governance and secure development practices.