DISC InfoSec blogAgentic AI Security Archives

Aug 06 2025

State of Agentic AI Security and Governance

Category: AI — disc7 @ 9:28 am

OWASP report “State of Agentic AI Security and Governance v1.0”

Agentic AI: The Future Is Autonomous — and Risky

Agentic AI is no longer a lab experiment—it’s rapidly becoming the foundation of next-gen software, where autonomous agents reason, make decisions, and execute multi-step tasks across APIs and tools. While the economic upside is massive, so is the risk. As OWASP’s State of Agentic AI Security and Governance report highlights, these systems require a complete rethink of security, compliance, and operational control.

1. Agents Are Not Just Smarter—They’re Also Riskier

Unlike traditional AI, Agentic AI systems operate with memory, access privileges, and autonomy. This makes them vulnerable to manipulation: prompt injection, memory poisoning, and abuse of tool integrations. Left unchecked, they can expose sensitive data, trigger unauthorized actions, and bypass conventional monitoring entirely.

2. New Tech, New Threat Surface

Agentic AI introduces risks that traditional security models weren’t built for. Agents can be hijacked or coerced into harmful behavior. Insider threats grow more complex when users exploit agents to perform actions under the radar. With dynamic RAG pipelines and tool calling, a single prompt can become a powerful exploit vector.

3. Frameworks and Protocols Lag Behind

Popular open-source and SaaS frameworks like AutoGen, crewAI, and LangGraph are powerful—but most lack native security features. Protocols like A2A and MCP enable cross-agent communication, but they introduce new vulnerabilities like spoofed identities, data leakage, and action misalignment. Developers are now responsible for stitching together secure systems from pieces that were never designed with security first.

4. A New Compliance Era Has Begun

Static compliance is obsolete. Regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 call for real-time oversight, red-teaming, human-in-the-loop (HITL) controls, and signed audit logs. States like Texas and California are already imposing fines, audit mandates, and legal accountability for autonomous decisions.

5. Insiders Now Have Superpowers

Agents deployed inside organizations often carry privileged access. A malicious insider can abuse that access—exfiltrating data, poisoning RAG sources, or hijacking workflows—all through benign-looking prompts. Worse, most traditional monitoring tools won’t catch these abuses because the agent acts on the user’s behalf.

6. Adaptive Governance Is Now Mandatory

The report calls for adaptive governance models. Think: real-time dashboards, tiered autonomy ladders, automated policy updates, and kill switches. Governance must move at the speed of the agents themselves, embedding ethics, legal constraints, and observability into the code—not bolting them on afterward.

7. Benchmarks and Tools Are Emerging

Security benchmarking is still evolving, but tools like AgentDojo, DoomArena, and Agent-SafetyBench are laying the groundwork. They focus on adversarial robustness, intrinsic safety, and behavior under attack. Expect continuous red-teaming to become as common as pen testing.

8. Self-Governing AI Systems Are the Future

AI agents that evolve and self-learn can’t be governed manually. The report urges organizations to build systems that self-monitor, self-report, and self-correct—all while meeting emerging global standards. Static risk models, annual audits, and post-incident reviews just won’t cut it anymore.

🧠 Final Thought

Agentic AI is here—and it’s powerful, productive, and dangerous if not secured properly. OWASP’s guidance makes it clear: the future belongs to those who embrace proactive security, continuous governance, and adaptive compliance. Whether you’re a developer, CISO, or AI product owner, now is the time to act.

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EU publishes General-Purpose AI Code of Practice: Compliance Obligations Begin August 2025

ISO 42001 Readiness: A 10-Step Guide to Responsible AI Governance

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

The Strategic Synergy: ISO 27001 and ISO 42001 – A New Era in Governance

Clause 4 of ISO 42001: Understanding an Organization and Its Context and Why It Is Crucial to Get It Right.

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The AI Readiness Gap: High Usage, Low Security

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DISC InfoSec’s earlier posts on the AI topic

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Tags: Agentic AI Governance, Agentic AI Security

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Jun 11 2025

Three Essentials for Agentic AI Security

Category: AI — disc7 @ 11:11 am

The article “Three Essentials for Agentic AI Security” explores the security challenges posed by AI agents, which operate autonomously across multiple systems. While these agents enhance productivity and streamline workflows, they also introduce vulnerabilities that businesses must address. The article highlights how AI agents interact with APIs, core data systems, and cloud infrastructures, making security a critical concern. Despite their growing adoption, many companies remain unprepared, with only 42% of executives balancing AI development with adequate security measures.

A Brazilian health care provider’s experience serves as a case study for managing agentic AI security risks. The company, with over 27,000 employees, relies on AI agents to optimize operations across various medical services. However, the autonomous nature of these agents necessitates a robust security framework to ensure compliance and data integrity. The article outlines a three-phase security approach that includes threat modeling, security testing, and runtime protections.

The first phase, threat modeling, involves identifying potential risks associated with AI agents. This step helps organizations anticipate vulnerabilities before deployment. The second phase, security testing, ensures that AI tools undergo rigorous assessments to validate their resilience against cyber threats. The final phase, runtime protections, focuses on continuous monitoring and response mechanisms to mitigate security breaches in real time.

The article emphasizes that trust in AI agents cannot be assumed—it must be built through proactive security measures. Companies that successfully integrate AI security strategies are more likely to achieve operational efficiency and financial performance. The research suggests that businesses investing in agentic architectures are 4.5 times more likely to see enterprise-level value from AI adoption.

In conclusion, the article underscores the importance of balancing AI innovation with security preparedness. As AI agents become more autonomous, organizations must implement comprehensive security frameworks to safeguard their systems. The Brazilian health care provider’s approach serves as a valuable blueprint for businesses looking to enhance their AI security posture.

Feedback: The article provides a compelling analysis of the security risks associated with AI agents and offers practical solutions. The three-phase framework is particularly insightful, as it highlights the need for a proactive security strategy rather than a reactive one. However, the discussion could benefit from more real-world examples beyond the Brazilian case study to illustrate diverse industry applications. Overall, the article is a valuable resource for organizations navigating the complexities of AI security.

The three-phase security approach for agentic AI focuses on ensuring that AI agents operate securely while interacting with various systems. Here’s a breakdown of each phase:

Threat Modeling – This initial phase involves identifying potential security risks associated with AI agents before deployment. Organizations assess how AI interacts with APIs, databases, and cloud environments to pinpoint vulnerabilities. By understanding possible attack vectors, companies can proactively design security measures to mitigate risks.
Security Testing – Once threats are identified, AI agents undergo rigorous testing to validate their resilience against cyber threats. This phase includes penetration testing, adversarial simulations, and compliance checks to ensure that AI systems can withstand real-world security challenges. Testing helps organizations refine their security protocols before AI agents are fully integrated into business operations.
Runtime Protections – The final phase focuses on continuous monitoring and response mechanisms. AI agents operate dynamically, meaning security measures must adapt in real time. Organizations implement automated threat detection, anomaly monitoring, and rapid response strategies to prevent breaches. This ensures that AI agents remain secure throughout their lifecycle.

This structured approach helps businesses balance AI innovation with security preparedness. By implementing these phases, companies can safeguard their AI-driven workflows while maintaining compliance and data integrity. You can explore more details in the original article here.