What agentic AI is, where the real risks live, and why this matters for governance professionals and AI executives alike

A recent SailPoint survey found that 80 percent of organizations have already encountered risky behavior from their AI agents; unauthorized system access, improper data sharing, even agents being manipulated into disclosing credentials. At the same time, Deloitte’s State of AI in the Enterprise 2026 report; surveying over 3,200 executives globally; found that only 21 percent of companies have a mature governance model for autonomous agents. Nearly three-quarters plan to deploy agentic AI within two years.

Eighty percent experiencing risky behavior. Twenty-one percent with mature governance. Whether you’re a governance professional building skills in this space or an executive overseeing AI strategy, that gap deserves your attention.

First, Let’s Define the Terms

Agentic AI refers to AI systems that can independently plan, make decisions, and take actions in pursuit of a goal; often with minimal human oversight. These systems browse the web, query databases, execute code, make API calls, send messages, and trigger downstream automations. Some spawn sub-agents that coordinate with each other. Many retain memory across sessions. McKinsey describes them as “digital insiders”; entities operating inside your systems with credentials, access, and decision-making authority.

If you’ve been working with conversational AI tools; ChatGPT, Claude, Gemini; you’re already building valuable skills. Agentic AI builds on that foundation, but adds something significant: the ability to take action in the real world without waiting for human approval.

Where the Risks Live

In December 2025, OWASP released the Top 10 for Agentic Applications, developed by over 100 security researchers. It’s quickly becoming the reference standard. Here are the risk areas that matter most through a governance lens:

• Agent Goal Hijacking; when external inputs (a document, email, or calendar invite) contain hidden instructions that redirect an agent away from its intended task. You don’t need code-level access; you redirect the agent through natural language.

• Tool Misuse; when agents use legitimate tools (APIs, databases) in ways that exceed their intended purpose through ambiguous prompts or manipulated input.

• Identity and Privilege Abuse; exploiting the credentials and delegation chains agents operate with. The average enterprise now has an 82-to-1 machine-to-human identity ratio, and it’s growing.

• Supply Chain Vulnerabilities; risks introduced through compromised tools, plugins, model weights, and protocols.

• Memory Poisoning; corrupting persistent data agents rely on for future decisions.

• Cascading Failures; where a single compromised agent propagates corrupted decisions through an entire multi-agent network. In one simulation, a single poisoned agent corrupted 87 percent of downstream decision-making within four hours.

Five Governance Principles for Securing Agentic AI

1. Least Agency

   The principle that agents should be granted only the minimum autonomy required for safe, bounded tasks. This is the agentic equivalent of least privilege, and it needs to be defined at the policy level.

2. Zero Trust for AI Agents

   No implicit trust, even within your own environment. Every action authenticated, every tool invocation validated, every inter-agent communication verified.

3. Human-in-the-Loop for High-Risk Actions

   Defining which decisions require human approval is a governance design decision that requires risk judgment.

4. Observability and Audit Trails

   Comprehensive logging of agent decisions, tool invocations, data access, and actions taken. You can’t govern what you can’t see.

5. Circuit Breakers and Kill Switches

   Every agentic system needs the ability to be stopped. NIST’s updated guidance recommends implementing circuit breakers that automatically cut access when agents exceed defined boundaries.

Why This Matters—for Governance Professionals and Executives

The skills required to govern agentic AI span well beyond the technical. What the OWASP framework calls for; defining acceptable autonomy levels, establishing approval workflows, monitoring agent behavior, managing non-human identities, evaluating third-party components; maps directly to policy, governance design, audit, identity governance, and supply chain risk management.

If you’re a governance professional, this is your territory. The domain knowledge can be learned; and most of the frameworks are free. If you’re an executive, understanding these risk categories helps you ask the right questions, allocate resources effectively, and build governance into your AI strategy before gaps become incidents. Deloitte’s research reinforces this: enterprises where senior leadership actively shapes AI governance achieve significantly greater business value than those delegating the work to technical teams alone.

Where to Start (For Free)

The OWASP Top 10 for Agentic Applications is the essential starting point. Add the NIST AI Risk Management Framework, the CSA MAESTRO Framework, and NIST IR 8596 (the Cybersecurity Framework AI Profile). All free. All authored by leading practitioners. You can build serious expertise without spending a dollar.

The frameworks are being built right now. The demand for people who can apply them; whether as practitioners or as leaders; is only growing.

Want to Build Your AI Governance Foundation?

My free 10-day challenge covers the core frameworks and skills. I also run a coaching program for professionals making this career transition and for executives who need to understand the AI governance landscape to lead more effectively. Subscribe for more: youtube.com/@obiogbanufe