Cybersecurity teams have always had to distinguish legitimate activity from malicious activity, but external AI agents make that judgment call harder. Unlike a conventional user session or scripted bot, an agent can pursue a goal across multiple steps, adapt as conditions change and operate at a speed and scale that can far exceed human activity.
The challenge grows when organizations are interacting with agents they didn’t build or deploy or don’t directly control. Those agents may be acting on behalf of customers, vendors or business partners—or probing systems for an attacker—and their behavior can evolve in real time. In a recent real-world example, AI agents run internally by OpenAI found an unintended path to the internet while trying to complete an evaluation, then breached Hugging Face systems in an effort to obtain information that could help them finish the task. The incident illustrates how quickly an autonomous system can move beyond the boundaries its operators expected.
For cybersecurity leaders, that raises a broader question: How do you defend systems when the actor at the other end may be autonomous, adaptive and outside your control? Members of the Senior Executive Cybersecurity Think Tank bring deep expertise in enterprise cybersecurity, risk management, threat detection and modern security architecture. Below, they explore how defensive strategy should evolve as external AI agents become more capable and which protections matter most when agent-driven activity originates beyond the organization.
“As AI agents become more capable, the challenge is less about blocking them and more about managing what they can access and do.”
Focus on Identity, Reach and Tracking
Bhavya Bhandari, Cybersecurity Risk Management Leader, Financial Services at Ernst & Young US LLP, has more than 15 years of experience leading security, regulatory and risk transformation programs for global financial services organizations. He explains that AI agents are changing the equation for security teams.
“As AI agents become more capable, the challenge is less about blocking them and more about managing what they can access and do,” Bhandari says.
With a change in focus comes a change in practical strategies.
“Cybersecurity leaders are increasingly focusing on strong identity controls, tight access management, API security and continuous monitoring,” Bhandari says. “The goal is to quickly spot unusual behavior, limit potential impact and build resilience as autonomous agents become a more common part of the threat landscape.”
Indeed, risk grows when agents are given too much freedom to act without strong checks on what they’re allowed to do. OWASP’s review of recent AI security incidents found that excessive autonomy, misconfigured permissions and weak validation controls can enable data theft, remote code execution and cascading failures. For security teams, limiting an agent’s reach can be as important as detecting malicious behavior in the first place.
Centralize the Response
Ken Grohe, President of LeverageGTM, Inc., brings more than 35 years of experience across SaaS, IT and security, including executive roles with global enterprises and startups. He says there are three critical controls for outside agent-driven activity.
“First is centralized command and control,” Grohe says. “Unify people, processes and communication channels into a single operating hub to maintain a shared operational picture.”
That kind of coordination becomes especially important when an incident spans multiple systems, teams and external partners. Effective incident response depends on a clear chain of command, defined responsibilities and established communication protocols so teams know who is responsible for decisions and updates as events unfold.
“Next is agentic response playbooks,” Grohe continues. “Deploy tailored, AI-guided response workflows that structure required inputs and execute coordinated tasks instantly.”
He concludes by stressing the importance of regulator-ready audit trails. Detailed logs can help incident responders reconstruct what happened, determine which systems or identities were involved and document the organization’s response for internal review, regulators and other stakeholders.
“Automatically document decisions, actions and communications across every stage of an incident to satisfy compliance and insurance demands,” Grohe advises.
Shift to Behavioral, Identity-Centric Monitoring
Kumar Ritesh, Founder, Chairman and CEO of CYFIRMA, has led cybersecurity work across intelligence, research, consulting and enterprise security. He says agent-driven threats require a strategy shift from perimeter defense to governing machine identities and predicting their next move.
“Defenders need to move from static, signature-based detection to behavioral, identity-centric monitoring, since AI agents adapt faster than rule-based defenses,” Ritesh says. “Treat every external agent, even those from trusted partners, as an untrusted identity making API calls, not a one-time login event.”
He details important restrictions and checks to put in place.
“Key controls include strict per-agent authentication and rate-limiting, least-privilege scoping so agents touch only what’s necessary, and anomaly detection across action sequences rather than single events, since agentic attacks unfold as chains. Output/action validation before execution and real-time access revocation on deviation matter too.”
Ritesh extends that approach beyond activity already occurring inside the environment.
“Equally critical is continuous external threat visibility with fast remediation, since agent-driven probing from outside can shift targets in minutes, not months,” he explains. “Predictive threat intelligence through feeding known attacker-agent behavior patterns into detection models helps teams anticipate moves before they happen rather than just reacting after compromise.”
Build Guardrails Before Agents Test Them
- Limit what every external agent can access and do. Use strong identity controls, least-privilege access and tightly scoped permissions so an agent can’t move freely through systems if its behavior changes or it’s compromised.
- Watch for behavior, not just known attack signatures. Continuous monitoring can help teams spot unusual activity early and contain its impact before an autonomous system moves further.
- Create a single command structure for agent-driven incidents. Clearly defined roles, communication channels and decision-making authority can help teams coordinate quickly when activity crosses systems, departments or external partners.
- Prepare response playbooks and audit trails before an incident occurs. Predefined workflows can speed coordinated action, while detailed records help teams reconstruct events and meet regulatory, insurance and internal reporting needs.
- Treat outside agents as untrusted machine identities. Authenticate each agent individually, restrict its privileges and monitor sequences of actions rather than assuming a trusted partner or vendor makes its agent trustworthy.
- Be ready to revoke access as behavior changes. Real-time anomaly detection, action validation and rapid remediation can help security teams respond when an agent deviates from expected behavior or shifts targets.
Defending Against Actors That Don’t Stand Still
External AI agents complicate cybersecurity because they combine machine speed and autonomy with access that may originate beyond an organization’s direct control. The response can’t depend solely on keeping agents out. Security teams also need to govern what agents can do, recognize unusual behavior quickly and coordinate their response when something goes wrong.
As agents become more capable, the distinction between identity, access and behavior is likely to become even more important. Organizations that establish clear limits, strong monitoring and practiced response processes now will have a firmer foundation for dealing with autonomous activity that may move faster—and behave less predictably—than the threats their existing defenses were designed to handle.
