The PBSAI Governance Ecosystem: A Multi-Agent AI Reference Architecture for Securing Enterprise AI Estates

2026-02-13 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

The Practitioners Blueprint for Secure AI (PBSAI) Governance Ecosystem is a multi-agent reference architecture designed to secure enterprise and hyperscale AI estates. As enterprises increasingly deploy large language models, retrieval augmented generation pipelines, and tool-using agents into production on shared computing clusters and cloud platforms, these systems form complex "AI estates" encompassing models, agents, data pipelines, security tools, human workflows, and infrastructure. Existing frameworks like the NIST AI Risk Management Framework offer principles but lack implementable architectures for multi-agent, AI-enabled cyber defense. PBSAI addresses this by organizing responsibilities into a twelve-domain taxonomy and defining bounded agent families that mediate between tools and policy using shared context envelopes and structured output contracts. It assumes baseline enterprise security capabilities and incorporates techniques such as analytic monitoring, coordinated defense, and adaptive response, with a formal model clarifying traceability and human-in-the-loop guarantees.

Key takeaway

For CTOs and VPs of Engineering deploying advanced AI systems, the PBSAI Governance Ecosystem provides a concrete architectural blueprint to secure your AI estate. Your teams should evaluate PBSAI's twelve-domain taxonomy and agent families to enhance traceability, ensure policy adherence, and integrate robust cyber defense mechanisms, moving beyond abstract principles to implementable security solutions for multi-agent AI deployments.

Key insights

PBSAI offers a multi-agent reference architecture for securing complex enterprise AI estates.

Principles

• AI estates require multi-agent governance.
• Security frameworks need implementable architectures.
• Traceability and provenance are critical for AI governance.

Method

PBSAI organizes responsibilities into a twelve-domain taxonomy and defines bounded agent families that mediate between tools and policy via shared context envelopes and structured output contracts, ensuring traceability and human oversight.

In practice

• Implement analytic monitoring for AI systems.
• Establish coordinated defense mechanisms.
• Develop adaptive response capabilities.

Topics

• AI Governance
• Multi-Agent Systems
• AI Security
• Reference Architecture
• NIST AI RMF

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.