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Guide · Digital Download

A practical playbook for governing data and securing AI systems — the controls, roles, and checks that keep your AI safe, compliant, and trusted as you scale it.

What you get

AI raises the stakes on data: more of it moves, into more systems, faster — and a single ungoverned dataset or unsecured model can turn into a breach, a compliance finding, or a loss of customer trust. This guide brings data governance and AI security together into one practical operating playbook. It covers who owns what, how data should be classified and accessed, the risks specific to AI and large language models, and the controls and checks to put in place — with templates you can adapt instead of building from scratch.

What’s inside

• Governance operating model & roles — data owners, stewards, and a governance council that actually functions.
• Data classification & access control — tiering data by sensitivity and enforcing least-privilege access.
• Privacy & regulatory compliance — practical alignment with GDPR, CCPA, and sector rules.
• AI & model risk management — evaluating, documenting, and monitoring models in production.
• LLM-specific security — prompt injection, data leakage, output handling, and safe retrieval.
• Third-party & vendor AI risk — the questions to ask before you trust an outside model or tool.
• Monitoring, logging & audit — the trail you need to prove control and catch problems early.
• Incident response for AI — a plan for when a model or dataset goes wrong.
• Policy & checklist templates — ready-to-adapt artifacts so you implement, not just read.

Who it’s for

CISOs and security leaders, data and privacy officers, IT and compliance teams, and the AI/ML leads responsible for deploying models safely. Written to be actionable for practitioners and clear enough to brief an executive.

Format & details

A digital download: the written playbook plus adaptable policy and checklist templates. One-time purchase — no subscription.

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