Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model

· Source: Machine Learning ML & Generative AI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, quick

Summary

Mend.io has released an 18-page guide titled "AI Security Governance: A Practical Framework for Security and Development Teams" to help organizations manage AI adoption securely. The framework addresses the common challenge of AI tools entering production before security teams are aware. It includes an AI asset inventory covering IDE tools, third-party APIs, open-source models, SaaS-bundled AI, internal models, and autonomous agents. The guide also details a five-dimension risk scoring system, an AI Bill of Materials (AI-BOM) extending the SBOM concept, three-layer monitoring for AI-specific threats like prompt injection and model drift, and a four-stage AI Security Maturity Model aligned with NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act.

Key takeaway

For AppSec leads and CISOs grappling with uncontrolled AI adoption, Mend.io's new framework offers a concrete playbook to establish governance. You should review its asset inventory, risk tiering, and AI-BOM concepts to proactively secure AI systems. Implementing its three-layer monitoring and aligning with its maturity model can help your organization get ahead of AI sprawl and mitigate emerging risks like prompt injection and model drift.

Key insights

Mend.io's framework provides a structured approach to AI security governance, from inventory to maturity.

Principles

Method

The framework outlines steps for AI asset inventory, five-dimension risk scoring, AI-BOM creation, three-layer threat monitoring, and progressing through a four-stage AI Security Maturity Model.

In practice

Topics

Best for: VP of Engineering/Data, AI Security Engineer, Director of AI/ML, CTO

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning ML & Generative AI News.