Managing AI adoption without stifling technical innovation
Generative AI and automated decision models offer immense productivity gains, but introduce novel security and regulatory vulnerabilities that traditional cybersecurity frameworks are ill-equipped to address. From confidential customer data leaking into public LLM training datasets to direct prompt injection attacks manipulating agentic workflows, organizations need concrete governance guardrails.
We help businesses audit their AI footprint, establish corporate acceptable use policies, evaluate third-party foundation model risk, and structure algorithmic oversight.
Four dimensions of our AI governance review
- Data Privacy & IP Protection: Auditing whether internal source code, customer records, or protected IP are being transmitted to foundation model providers without enterprise zero-retention agreements.
- Adversarial Security (OWASP Top 10 for LLMs): Testing agentic workflows and prompt interfaces against direct/indirect prompt injection, training data poisoning, and unauthorized tool invocation.
- Human Oversight & Decision Logging: Establishing verifiable audit logs for automated decisions affecting users, ensuring human-in-the-loop controls are operational.
- Vendor & Foundation Model Risk: Reviewing terms of service, SLA reliability, and data privacy commitments of third-party model providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex AI).