AI Security Standards
CIS Controls, CIS Benchmarks, AI Companion Guides, MCP Benchmark work, and practical control mapping for emerging AI systems.
AI Security Standards & Enablement
Securing the space between AI capability and real-world action.
I help organizations adopt AI safely by translating LLM, agentic AI, MCP, and RAG risks into practical security guidance, benchmarks, tools, and enablement programs.
$ whoami andrew-dannenberger $ cat profile.json { "focus": "AI Security Standards & Enablement", "standards": "Principal Co-author, CIS v8.1 MCP Guide", "specialties": ["MCP", "CIS Controls", "Agentic AI"], "based": "Chicago Area / Remote", "currently": "Building a CIS MCP Benchmark" } Modern AI systems move from model inference through agentic reasoning, MCP tool calls, and data retrieval — gated by human review — before taking real-world action. Understanding this flow is the foundation of AI security.
A few convictions that shape how I approach standards, tools, and enablement.
Principles tell teams what to care about; controls tell them what to configure, check, and audit. Risk only drops when guidance gets specific enough to act on — which is why I work in benchmarks and control mappings, not abstractions.
Prompt injection, confused-deputy access, over-scoped tools — the dangerous failures live at the seams between instruction and data, model and tool, agent and action. Securing those boundaries matters more than hardening the model alone.
The fastest way to create shadow AI is to make the safe path the hard path. Giving people usable, well-governed ways to adopt AI prevents more real-world risk than a strict policy that quietly gets ignored.
Human-in-the-loop is most valuable exactly when an agent can take an action it cannot take back. Designed deliberately, it is a control point worth keeping — not friction to engineer away.
Bridging the gap between AI capability and responsible adoption — through standards, benchmarks, and practical enablement.
CIS Controls, CIS Benchmarks, AI Companion Guides, MCP Benchmark work, and practical control mapping for emerging AI systems.
Tool governance, least privilege, prompt and resource exposure, approval flows, confused deputy risk, and auditability across MCP ecosystems.
AI Office Hours, leadership briefings, customer education, practical adoption guidance, and security-aware AI workflows for all skill levels.
Chatbots, RAG, knowledge systems, SecureSuite Platform MCP concepts, and secure AI implementation patterns.
Standards authorship, benchmarks leadership, and enablement programs.
Center for Internet Security · April 2026
Translated MCP-specific risks — tool invocation, authorization, confused-deputy patterns, and third-party server governance — into practical CIS Controls-aligned guidance across all 18 Controls.
Center for Internet Security · Ongoing
Leading community work to advance practical guidance for emerging AI technologies, including scope definition, coordination, feedback cycles, and publication strategy.
Center for Internet Security · Ongoing
Designed and leads AI enablement sessions for beginner and advanced users, turning complex AI security topics into practical workflows and adoption guidance.
Interested in conversations about AI security advisory, standards authorship, enablement leadership, and organizations building serious AI security programs.