Lead Agentic AI That Legal and Security Trust

Distinguish agents from simpler automation and compare build, buy, and pilot options.

Map data, tools, memory, retrieval, MCP, and agent handoffs to show where decisions and actions occur.

Create an agent profile and business case with measurable benefits, costs, ownership, and stop conditions.

Assess privacy, security, reliability, fairness, and business harms for a specific agent use case.

Use NIST AI RMF and ISO/IEC 42001 to organize risks, ownership, controls, and evidence.

Identify applicable legal questions and vendor evidence gaps for review with qualified stakeholders.

Apply VERDICT to assign accountable owners, risk acceptance, human approval, and escalation responsibilities.

Specify what an agent may read, write, send, or spend, including delegated access and prohibited actions.

Create an authority matrix linking each consequential action to an owner, control, and evidence requirement.

Review prompt injection, data leakage, tool misuse, and multi-agent handoff risks through a continuing case.

Define acceptance thresholds and challenge test results, vendor claims, and missing evidence.

Build a control and evaluation plan with failure cases, human review, and release criteria.

Assemble the agent profile, risks, obligations, controls, test evidence, and residual-risk decisions.

Practice a simulated review and explain why the agent should be approved, restricted, or rejected.

Use a capstone rubric to identify gaps and revise your governance package after feedback.

Set monitoring thresholds, cost limits, escalation owners, shutdown conditions, and recovery requirements.

Decide when changes to models, prompts, tools, data, or permissions require renewed evaluation and approval.

Prepare an operating plan that keeps people accountable as the agent and its environment change.