A framework for securing agentic AI in production
Build agentic AI you can actually deploy.
Threat modeling, runtime policy enforcement, least-privilege tool access, and end-to-end auditability.
Prompt injection Tool abuse Memory poisoning Policy enforcement Auditability
AISecOps in one line
Secure agentic systems by constraining capabilities, validating context, enforcing runtime policy, and proving outcomes.
Layer 1
Context
Trust boundaries for retrieval, memory, and data sources.
Layer 2
Capability
Least-privilege tool access and action authorization.
Layer 3
Execution
Sandboxing, change control, and human-in-the-loop gates.
Layer 4
Observability
Trace prompts → tools → outcomes with audit logs.
Reference Architecture
A layered blueprint: context validation, capability controls, execution boundaries, and observability.
Enterprise Adoption
How to roll out AISecOps in regulated environments: governance, auditability, and operating model.
Open Source
Reusable building blocks: policy engine patterns, gateways, test harnesses, and example agents.
Blog
Implementation notes, checklists, and field-tested patterns for agentic AI security.