Applied Lab
DemonstratingAgentic Security explores the controls required when AI systems move from passive generation to active participation in enterprise systems.
What I’m exploring
Agents can combine instructions, identity, memory, data, tools, and delegated authority. The Lab examines how existing enterprise security capabilities must extend across that chain without treating agentic security as a separate security system.
Framework → Lab
The capability model describes what an enterprise needs. This Lab creates practical evidence across the highlighted areas below.
Provides the broader Govern, Identify, Protect, Detect, Respond, and Recover context.
Defines agent lifecycle, autonomy, evaluation, and human-machine work.
Defines risk ownership, acceptable use, oversight, and accountability.
Places identity, policy, mediation, observation, and containment into the system design.
What I’ve applied
The work combines implementation patterns, controls, evaluation, and current product evidence; it does not rely on expertise claims alone.
External content is treated as untrusted evidence. Source access is host-bound and budgeted; cases and actions are tenant-bound; publication remains a separate human-authorized transition; production execution fails closed.
How agents, workloads, services, users, and tools are identified and authenticated.
What an agent may retrieve, invoke, change, or retain—and under which conditions.
How untrusted content, indirect instructions, and information access can alter intended behavior.
Which actions require authorization and whether activity can be reconstructed end to end.
What I’m learning
These are working lessons for technology leaders evaluating how AI moves from experiments into secure, governed production work.
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