AI Access Control, Logging, and Retention Policies
How to design access controls, prompt/output logging, and retention rules for AI systems so governance remains practical, auditable, and proportional to risk.
Privacy & Deployment
AI Evaluation, Monitoring, and Incident Response for Production Systems
How to evaluate, monitor, and respond to failures in production AI systems so quality, safety, and governance remain active after launch.
AI Vendor Risk Assessment and Procurement Checklist
How to evaluate AI vendors before rollout, using a practical checklist for data handling, governance, contract risk, security posture, and operational fit.
Anonymize Customer Data with AI
How to use AI to redact, mask, or pseudonymize customer data safely, and where automated anonymization can fail in practice.
Deploy Your Own Private LLM
What a private LLM deployment means in practice, when it makes sense, and how to compare managed private inference, self-hosting, and hybrid architectures.
How to Design Human Review for AI Systems
How to build a risk-tiered human review model so oversight is meaningful, efficient, and matched to business impact rather than added as a vague slogan.
Open-Source LLMs You Can Host
How to choose a hostable open-weight model based on task fit, hardware limits, governance needs, and support burden rather than hype.
When Not to Send Data to a Public LLM
How to decide when a business workflow should avoid public LLM endpoints, based on data sensitivity, contractual exposure, and safer design alternatives.