Agentic AI systems are designed to operate autonomously to achieve a goal, interpreting objectives, selecting tools, executing multistep tasks across systems, and adapting their approach based on intermediate results. Unlike traditional software that follows fixed instructions, agentic AI can make decisions, delegate to other agents and take actions with real-world consequences, often with minimal or no human intervention at each step.
Enterprises are adopting agentic AI at a rapid pace, drawn by its ability to compress complex workflows ranging from IT operations and software development to customer service and financial processing. These automated pipelines run faster and at a greater scale than human teams alone. But that same autonomy introduces a security challenge that conventional frameworks weren’t built to handle.
Traditional access controls were designed around a simple premise: Verify the user or system, then permit or deny the action. In agentic environments, that model breaks down. An agent may be fully credentialed and operating within approved systems yet still drift into behavior that no one explicitly authorized. That’s why intent-based security is quickly becoming a core consideration for enterprise AI adoption.
For security leaders, the challenge is building controls that are strong enough to prevent harm without slowing the very automation they’re trying to enable. Members of the Senior Executive Cybersecurity Think Tank have deep expertise in enterprise cybersecurity strategies, risk management, regulatory compliance and modern security architecture. Below, three of them discuss why intent matters in agentic environments and which runtime signals and safeguards leaders should prioritize as autonomous systems become more deeply embedded in business operations.
“In that world, ‘Who are you?’ isn’t enough. You also need to know, ‘What are you trying to do right now, and should you be allowed?’”
Watch for Intent Drift in Fast-Moving Systems
Maman Ibrahim, Founder of Ginkgo Resilience LTD, frames the issue in practical terms: In agentic AI systems, security becomes far more dependent on context.
“Intent-based security matters because agents don’t just access data. They take action. They book, delete, deploy, pay, message and escalate,” Ibrahim says. “In that world, ‘Who are you?’ isn’t enough. You also need to know, ‘What are you trying to do right now, and should you be allowed?’”
For security leaders, the question becomes less about whether an agent has permission in the abstract and more about whether its current behavior still matches the business intent behind that permission.
“At runtime, watch for intent drift and risky patterns,” Ibrahim says. “These include unusual data scope, sudden privilege requests, rapid tool chaining, cross-tenant queries, outbound sharing and retries that look like persistence.”
Those signals can give security teams a clearer view into whether an agent is still operating within safe boundaries or starting to behave in ways that require intervention. Noting that “fast agents need faster brakes,” Ibrahim lists some effective protections.
“Safeguards that help include scoped tools, allow-listed actions, policy-as-code gates, step-up approval for high-impact moves, and immutable logs you can audit later,” he says.
“The real question is not whether the credential is valid, but whether the action still matches the task, scope and context it was authorized for.”
Prioritize Alignment as Much as Authentication
As Co-Founder and COO of CompFly AI, Anand Salodkar built a business that provides the operational layer to discover AI agents and enforce tool boundaries at runtime. He points to a critical weakness in traditional security thinking: A valid credential signals a “safe” actor. In agentic environments, identity and authorization still matter, but they don’t tell the whole story.
“Intent-based security matters because agents can remain authenticated while becoming misaligned,” Salodkar says. “The real question is not whether the credential is valid, but whether the action still matches the task, scope and context it was authorized for.”
That’s a subtle but important shift. A human user understands edge cases and variables and how to verify and adapt to them, but an AI agent can carry over context from earlier tasks and reinterpret goals—and the pathways to achieve them. Salodkar describes a hypothetical case.
“A procurement agent is allowed to compare quotes,” he says. “It picks up manipulated pricing context from a prior run and starts steering toward the wrong vendor. Its credential is valid, but its intent is no longer aligned. An intent-based control layer catches the drift and blocks or escalates the action.”
For enterprise security teams, that means evaluation has to happen continuously—not just at login, launch or initial approval.
“Key runtime signals include task-bound identity, execution scope, delegation path, memory state, context integrity and boundary crossings,” Salodkar says. “Key safeguards are ephemeral identity, narrowing permissions, continuous evaluation and real-time enforcement.”
“Traditional security controls focus on what an action is—an API call, a privileged use—but in agentic systems, the same action can be safe or harmful depending on why it is being taken.”
Understand That Context Changes Risk
Bhavya Bhandari, Cybersecurity Risk Management Leader, Financial Services at Ernst & Young US LLP, says the growing importance of intent-based security comes down to a core reality of agentic systems: The same action can carry very different risks depending on why it’s happening.
“One key reason intent-based security is critical in agentic environments is that agents are no longer executing fixed commands,” Bhandari says. “They are making decisions, chaining actions and adapting behavior autonomously.”
That autonomy changes how leaders should think about security controls. In a traditional environment, a control may evaluate whether a given action is allowed. In an agentic environment, that assessment is still necessary, but it may not be sufficient.
“Traditional security controls focus on what an action is—an API call, a privileged use—but in agentic systems, the same action can be safe or harmful depending on why it is being taken,” Bhandari says.
Key Moves for Security Leaders
- Evaluate what the agent is trying to do, not just whether it has access. A credentialed agent can still drift outside safe boundaries, so runtime controls should account for current behavior, business intent and context.
- Watch for signals that an agent’s behavior is expanding or changing. Unusual data scope, sudden privilege requests, rapid tool chaining, outbound sharing and repeated attempts can all indicate that an agent needs closer review or intervention.
- Treat alignment as a continuous requirement. Authentication should only be the starting point; security teams need ongoing evaluation of whether each action still matches the task, scope and context originally authorized.
- Build safeguards that can slow or stop high-risk actions in real time. Scoped tools, allow-listed actions, policy-as-code gates, step-up approvals, narrower permissions and real-time enforcement can help preserve speed without handing agents unchecked authority.
- Make context central to risk decisions. In agentic environments, the same API call, data request or privileged action may be safe in one situation and harmful in another, depending on why it’s being taken.
Building Trust Into Autonomous Systems
Agentic AI can drive new levels of speed and productivity, but it will also push enterprises to rethink security in terms of purpose, context and continuous evaluation. Traditional access controls still matter, but they can’t answer the most important runtime question on their own: Does this action still make sense given the agent’s approved goal?
As autonomous systems take on more complex work, intent-based security will become essential to building trust at scale. Organizations that can evaluate intent in real time, detect drift and apply the right safeguards will be better positioned to capture the benefits of agentic AI without allowing speed to outpace control.
