AI foundations
Before you govern AI, understand the system.
A model is one component. The people, data, tools, and decisions around it determine much of the risk.
Read and explore ↗Knowledge hub / THE KNOWLEDGE LIBRARY
THE KNOWLEDGE LIBRARY
A library for better AI decisions.
Explore practical guides, source explainers, learning paths, and exercises. Follow your question rather than a prescribed reading order.
16 guides
Governance & accountability
You cannot govern the AI you cannot see.
Build an AI inventory people can maintain, including the features already hiding inside everyday software.
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Governance & accountability
Human oversight needs a job description.
A person in the loop is useful only when they have time, authority, and enough information to intervene.
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AI risk management
An impact assessment starts with people.
Ask who could be affected, how the system changes a decision, and what would make its use unacceptable.
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AI risk management
Write risks people can make decisions about.
A good risk register explains a scenario, a consequence, and an accountable response. It does not need theatrical scoring.
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AI risk management
Buying an AI service also buys dependencies.
Ask how the service behaves, what the contract covers, and what you will do when either changes.
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Compliance & standards
Good AI governance follows the data.
Map personal information, sensitive business material, and derived copies before deciding how to protect them.
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AI security
An AI agent needs clear boundaries.
Keep user intent, application decisions, and tool permissions separate when a system can take action.
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AI security
When a document tries to give orders.
Prompt injection makes the boundary between useful information and trusted instructions difficult to maintain.
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AI security
A knowledge assistant should respect the original door locks.
Retrieval-augmented generation creates new paths to existing information. Preserve permissions along those paths.
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AI security
AI changes offensive security. Accountability still applies.
Understand how AI supports attackers and authorized testers without confusing a demonstration with a reliable autonomous capability.
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Assurance & testing
A useful evaluation answers a decision.
Choose tests that tell you whether this system is suitable for this task, under conditions that matter.
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Assurance & testing
Red teaming should change the design.
A structured adversarial exercise finds weaknesses and produces evidence that teams can act on.
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Assurance & testing
Prepare for an AI incident before the first escalation.
Connect familiar response disciplines to models, prompts, retrieval systems, and autonomous actions.
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Compliance & standards
A framework mapping is a conversation starter.
Understand what laws, standards, controls, and tests each establish before connecting them.
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Governance & accountability
Give AI governance a place in everyday work.
Connect decisions to procurement, engineering, security, and business ownership so the process survives beyond launch.
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