Robert Hayes
The practical security guidance looks solid, though more pricing details would help confirm suitability for our specific enterprise adoption needs right now.
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Sarah Jenkins
The approach to AI security here is remarkably grounded in implementation rather than abstract principles. The work on the CIS Controls companion guides for MCP, AI/LLM, and AI Agents demonstrates a thorough mapping of risks to specific controls. What stands out is the integration of enablement programs alongside technical standards, ensuring that security guidance is not just documented but actually usable by diverse teams. The background in training and the current role leading benchmarks suggests a strong ability to translate complex protocols like MCP into practical workflows. For organizations serious about adopting AI safely, this blend of standards authorship and hands-on enablement seems like a robust foundation. The focus on human-in-the-loop controls and boundary security addresses real-world deployment issues effectively.
David Miller
Reading through the work available, it is impressive how the focus stays on making AI security actually adoptable rather than just theoretical. The emphasis on enablement as a security strategy really stands out. Translating complex risks into concrete controls for teams seems like a strong value proposition. The detailed guides and benchmarks suggest a deep understanding of where AI systems actually fail, which is exactly what organizations need right now.
Margaret Lin
The expertise in MCP security and CIS controls is clearly highlighted. However, it is not entirely clear what specific services or consulting engagements are included under the general enablement offerings. The service offering seems comprehensive, yet for someone considering this company, the exact scope and deliverables for a specific project would need checking first.
Elias Thorne
The standards work seems practical and well-mapped to real controls. From the available information, this approach looks reliable for organizations needing clear, implementable AI security benchmarks.
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Andrew Dannenberger is an individual professional focused on AI security standards and enablement, based in the Chicago area and working remotely. He helps organizations adopt AI safely by translating risks related to large language models, agentic AI, the Model Context Protocol (MCP), and retrieval-augmented generation into practical security guidance, benchmarks, tools, and enablement programs. He is the principal co-author of the CIS Controls v8.1 MCP Companion Guide and has collaborated on related CIS Controls guides for AI/LLM and AI agents. His work includes presenting in webinars and podcasts on enterprise AI security topics, and he is currently building a CIS MCP Benchmark. The site describes him as open to AI security standards and enablement roles.
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