As artificial intelligence continues to transform all aspects of our economy, business leaders from every industry have been seeking ways to improve competitiveness and take advantage of the business value of these new technologies. Some of the most impactful variants of AI are those known as Agentic AI. This approach, which involves the use of software that can act independently, has been around for over two decades, but has recently been empowered by the rise of Generative AI, Large Language Models and Natural Language Processing. All indications are we are going to face a Cambrian Explosion in Agentic AI solutions for enterprises.

The Promise and the Peril of Agentic AI

Agentic AI systems go beyond traditional AI-powered apps. They plan, adapt, use tools, call APIs, interact with legacy systems, and maintain persistent memory. Agentic AI agents can interact with each other and take action in pursuit of their assigned goals. Their autonomy and operational reach deliver outsized business value, accelerated workflows, responsiveness at scale, smarter automation, and the promise of true digital transformation. However, this autonomy and interconnectedness radically expand the attack surface and introduce a new class of vulnerabilities, including:

• Reasoning Manipulation: Agents can be subtly steered to incorrect goals or “reasoning path hijacking” making bad decisions with apparent logic.
• Persistent Memory Poisoning: Attackers can plant falsehoods that linger, corrupting the agent’s future actions and outputs.
• Unbounded Tool Use: Given autonomy to invoke APIs or code, agents can be tricked into chaining benign actions for malicious outcomes, for example exfiltrating data, causing financial loss, or bypassing controls.
• Privilege Escalation and Identity Spoofing: Fluid digital identities, where agents act on behalf of users or other agents, open the door to privilege abuse and impersonation attacks.
• Governance Blind Spots: Autonomous agents generate vast volumes of events and alerts, easily overwhelming human reviewers and hiding real threats in a sea of noise.

The Corporate Imperative: From Hype to Hard Controls

Leaders, CEOs, boards, CISOs, and operational executives, should accept that Agentic AI is fundamentally different from older digital technologies. Traditional IT security controls alone are still important but they are not enough. AI failures are not hypothetical: flawed reasoning, bias, hallucinations, and vulnerability to manipulation have been seen across sectors, from financial algorithms to autonomous hiring and law enforcement.

The experiences of the OODA Network, which consists of a wide range of security and artificial intelligence practitioners, has generated extensive first-hand knowledge relevant to mitigating the risks of Agentic AI solutions. We have seen first-hand how prudent planning can optimize the value of Agentic AI system while mitigating risk. We have seen many, including NIST and MITRE, work across broad communities to build frameworks meant to apply controls to risks in agentic AI, but have seen very little uptake in their application in real world situations. Our approach to motivating action to reduce risk is to advocate for risk mitigation at the concept phase and a red team based review of systems throughout the lifecycle. This requires a solid understanding of what is unique about Agentic AI and focused thought on what is different about the attack surface.

Threat Modeling Agentic AI:

Agentic AI systems run on technology so all our old methods of protection must still be put in place. However, there are many unique risk domains and a need for new controls as well.

Risk Domain	Key Threats	Essential Controls
Cyber Security	Traditional threats to both internal infrastructure and cloud systems pose significant risks to Agentic AI since adversaries see them as a prime target	The foundational security controls required by modern enterprises remain critically important.
Cognitive Security	Reasoning path hijacking, goal drift	AI behavior profiling, anomaly detection in decision logic, runtime goal/plan validation
Memory/Knowledge Integrity	Memory poisoning, belief loops	Cryptographic memory integrity, versioning, cross-agent validation, forensic rollback
Operational Execution	Tool abuse, privilege escalation	Segmentation, sandboxed tool use, API rate limiting, real-time privilege validation, JIT access
Trust Boundary	Identity spoofing, trust abuse	CIAM for agents, multi-factor for privileged actions, mutual authentication between agents
Governance/Circumvention	Alert fatigue, oversight bypass	Decentralized oversight (distributed reviewers), tamper-proof and immutable logs, explainable AI

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Some of our critical lessons include the key principles corporate Agentic AI risk mitigation programs should be built around. They include:

1. Begin with security in mind. All aspects of traditional security activities and controls still apply to Agentic AI. Without a strong foundation all bets are off!
2. Executive Engagement and Vision: Responsibilities for mitigating AI risk should be assigned to leaders with the ability to understand and the power to act. This may be the senior technologist in the organization but in most cases will also include line of business owners. Governance should be in place to ensure all executives understand their role and required actions. Senior leadership should define the strategic vision, set risk appetite, and demand transparency, oversight, and measurable goals throughout the AI lifecycle.
3. Organizational Architecture: Think beyond central IT. Consider hybrid team structures that blend business, legal, compliance, and technical expertise. Decide early on the right level of AI transparency and explainability needed for your regulators, industry, and brand.
4. Data, Algorithm, Identity, and Infrastructure Controls: Protect data from manipulation, ensure proper data lineage and governance, demand algorithmic transparency (to the extent possible), and manage identities, especially non-human identities (service accounts, agents, bots) with least privilege and constant validation.
5. Continuous Monitoring and Specialized Oversight: Traditional incident response is too slow for Agentic AI. Invest in specialized monitoring (heuristic, AI-driven) for anomalies in agent behavior, resource use, tool chains, and privilege transitions.
6. Zero Trust and Segmentation: Apply zero-trust principles to AI agents. Micro-segment access to tools, data, and actions; use just-in-time privileges; and sandbox tool execution to contain blast radius.
7. Independent Audit and Red Teaming: Commission third-party reviews (before and after deployment) to test for bias, logic errors, and exploitability. Periodically run adversarial “red team” simulations to expose weaknesses and test controls.

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Mitigation Examples in Action

• Prevent Reasoning Manipulation: Use AI-driven monitoring to baseline “normal” agent behavior, then detect/freeze deviations in plan/goal logic.
• Stop Memory Poisoning: Enforce cryptographic validation and provenance for persistent agent memory and RAG databases; require cross-source validation before knowledge commits.
• Control Tools and Execution: Restrict agent access to only necessary tool APIs, use sandboxes for risky operations, implement explicit human approval for financial or critical ops.
• Harden Identity and Privilege Flows: Implement least-privilege policies for all agent identities, rotate secrets/tokens frequently, and monitor for privilege escalations and anomalous identity transitions in real time.
• Scale Oversight: Use dynamic risk-based review queues, automate low-risk tasks, but require human or multi-agent consensus for high-impact or ambiguous actions.
• Crush Governance Gaps: Store all agent logs immutably and cross-check agent actions for repudiation attempts; require periodic independent audits and red team exercises

What’s Next: Embedding Resilience and Competitive Advantage

Agentic AI, if secured and governed well, can be a true force-multiplier, creating competitive advantage, unlocking new markets, and driving innovation. But only if risk is proactively managed. Companies that treat these systems as “just software” will face new classes of attack, cascade failures, and likely regulatory scrutiny.

Recommended Actions for Leaders:

• Number One Recommendation: Conduct an external assessment of your Agentic AI approach
• Make Agentic AI risk a standing topic for board oversight.
• Require operational, red-team tested mitigation plans for every AI deployment.
• Insist on comprehensive supply chain audits for agentic frameworks and third-party models.
• Invest continually in upskilling teams on the latest AI threats, controls, and governance models.
• Build using best practices and best in class platforms.

By pairing innovation with disciplined controls, forward-thinking enterprises can realize the benefits of Agentic AI, while dramatically reducing the downside risk.

Additional Resources:

• When AI Goes Wrong
• The Executive’s Guide to Artificial Intelligence
• Using Artificial Intelligence For Competitive Advantage in Business