Enterprise practice
AI Security & Governance
AI capability is moving faster than security, policy, and operational control. Teams cannot answer which data a system can access, which actions it may take, or how unsafe behavior will be detected.
When organizations bring us in
The commercial trigger
Agents touch sensitive data, call tools, influence decisions, cross trust boundaries, or must pass security and risk review.
Who owns the problem
Accountable technology leadership
CIO, CISO, data, risk, and product leaders accountable for governed AI adoption.
What we engineer
Technical controls that make AI behavior reviewable, bounded, and operable.
AI threat models and abuse cases
Prompt, model, tool, and data trust boundaries
Identity and authorization for agents
Adversarial and policy evaluation suites
Audit events, approvals, and escalation workflows
What makes it difficult
Consequence changes the technical work.
AI systems expand the attack surface through natural-language inputs, retrieved data, external tools, and probabilistic outputs. Governance in policy documents alone cannot constrain runtime behavior.
How we approach it
Architecture through controlled release.
- Map data, model, tool, and user trust boundaries
- Translate material risks into enforceable runtime controls
- Test prompt injection, data leakage, excessive agency, and failure handling
- Capture evidence that security, risk, and engineering teams can review
Concrete outputs
Artifacts teams can build, operate, and govern.
AI threat model and control map
Prompt and tool authorization boundaries
Evaluation and red-team suites
Audit events and escalation paths
Priority applications
Where this practice carries particular consequence.
Relevant engineering work
Comparable problems and system scope.
Related engineering depth
Explore the underlying capabilities.
Next step
Bring us the system, constraint, and consequence.
An engineer will assess the technical fit and the next useful decision.