[Guides]
Playbooks for AI governance.
Practical, vendor-neutral guides to help you discover, secure, and govern AI across your company.
[More about AI]
The Agentic AI Security Framework
Our open model for securing autonomous AI across models, MCP, the browser, and coding agents.
A control model for autonomous AI
Agents do not just answer questions, they read data, call tools, and act on systems. The Agentic AI Security Framework defines the surfaces, threats, and controls every organization needs to govern them, mapped to ISO 42001, the EU AI Act, and SOC 2.
White paper
Agentic AI Security Framework
v1.0 · Cerbera Research
[Library]
Guides and playbooks.
The shadow AI discovery playbook
A step-by-step method to inventory every AI tool, agent, and MCP server in your org in under a week.
Securing AI coding agents
How to let engineers use Cursor, Claude Code, and Copilot without exfiltrating source or secrets.
Vetting MCP servers before they connect
A risk-scoring rubric for evaluating MCP servers and detecting rogue ones in flight.
Building an AI acceptable-use policy
A template and rollout plan that keeps teams fast while satisfying security and legal.
Getting ready for ISO 42001
What the AI management system standard expects, and the controls you can deploy today.
DLP for the AI era
Why prompt-level data loss prevention matters and how to deploy it across browser, IDE, and CLI.
The MCP supply chain: a new class of risk
How autonomous agents inherit the permissions of every MCP server they touch, and a model for governing them.
Prompt-level DLP for the AI era
Why traditional data loss prevention misses AI traffic, and how transparent proxying closes the gap.
Measuring shadow AI
A behavioral methodology for discovering unsanctioned AI tools across an organization.