AIGN Agentic AI Governance Framework 1.0

The AIGN Agentic AI Governance Framework 1.0 is the first certifiable governance model focused on autonomous and multi-agent AI systems. It tackles emerging risks, compliance demands, and operational needs as AI agents rapidly integrate into workflows and critical infrastructures.

Key Features of AIGN:

✅ Trust & Capability Indicators: Tools for goal alignment, oversight, escalation, and accountability.

✅ Governance Maturity Model: 5-tier roadmap from ad hoc governance to adaptive, self-regulating oversight.

✅ Integrated Risk Mapping: Agentic Risk Assessment Tools (ARAT), heatmaps, and scenario planning for vulnerabilities like goal drift or agent collusion.

✅ Compliance-By-Design: Aligns with EU AI Act, ISO/IEC 42001, NIST RMF, and OECD AI Principles for regulatory trust.

✅ Operational Toolkit: Includes Trust Scans, Goal Alignment Canvas, Multi-Agent Interaction Matrices, Continuous Monitoring APIs, Red Team Templates, and Learning Loops.

Why Governance Is Crucial for Cybersecurity:

Palo Alto Networks Unit 42 highlights 10 critical AI agent vulnerabilities:

Authorization & Control Hijacking: Exploiting weak access controls to hijack agent functions.

Critical System Interaction: Attacks through unsecured interfaces between agents and core systems.

Goal & Instruction Manipulation: Altering agent objectives to trigger harmful or unintended actions.

Hallucination Exploitation: Leveraging AI-generated false data to mislead decision-making.

Impact Chain & Blast Radius: One agent compromise cascading across multi-agent ecosystems.

Knowledge Base Poisoning: Inserting malicious data into agent knowledge sources.

Memory & Context Manipulation: Extracting sensitive data from agent memory or session context.

Multi-Agent Exploitation: Exploiting poorly controlled agent-to-agent orchestration.

Resource Exhaustion Attacks: Overloading agents to degrade service or trigger denial-of-service.

Supply Chain Attacks: Targeting vulnerable dependencies or open-source agent components.

AI agents are in production environments, managing workflows and interfacing with critical systems. But we’re already seeing real-world attacks exploiting vulnerabilities. The AIGN framework is critical because it embeds structured governance, continuous risk mapping, and trust indicators—essential as we align with other more known requirements like AIAct, ISO42001, NISTRMF, or OECD. Organizations must deploy goal bounding, monitoring APIs, and agent isolation measures to contain blast radius if an attack succeeds.

This is where AI governance needs to go: continuous oversight, security integration, and auditable compliance to counter evolving agentic threats.

Link to the AIGN framework

Author: Sebastian Burgemejster