How security leaders can safely and effectively implement agentic AI

2025 began with experts warning about the dangers of agentic AI use—but that didn’t slow adoption. Our annual State of Trust Report shows that nearly 80% of organizations are either actively using or planning to use agentic AI. That acceleration is outpacing the governance required to keep these systems safe:

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• 61% of organizations are willing to let an AI agent override a human decision in certain scenarios
• 71% are comfortable with agents contributing to high-level security strategy
• 48% have developed frameworks to limit or define agent autonomy

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A level of machine autonomy that would’ve been unthinkable just a few years ago is quickly becoming normalized. 61% of organizations are comfortable with agentic AI overriding a human decision in certain scenarios, and leaders are increasingly willing to let autonomous systems influence, or even determine, business-critical outcomes.

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This shift comes with meaningful risk.

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The problem isn’t that agentic AI is inherently unsafe. It’s that organizations are adopting it before they’ve established the guardrails needed to manage it. Without a clear governance framework, agentic AI introduces real exposure across systems, data, and workflows.

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AI agents can help teams reclaim time and improve security, but only when grounded in strong governance, human oversight, and clearly defined controls. That’s where most organizations fall short today, and it’s where the biggest opportunity for building trust lies.

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How today’s agentic AI adoption is increasing risk

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Agentic AI already powers a growing set of operational and security workflows, from customer support routing to automated procurement decisions. In security, leaders report using agentic AI to automate high-volume, high-velocity tasks across their programs, particularly for activities that require continuous monitoring and fast pattern recognition, such as:

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• Forensic log analysis and timeline reconstruction: 35%
• Automated threat correlation and anomaly detection: 33%
• Generating compliance reports and audit logs: 31%
• Automated vulnerability scanning and prioritization: 31%

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Comfort with autonomy is also rising overall:

• 61% of organizations are willing to let agentic AI override a human decision in certain scenarios
• 71% are comfortable with agents contributing to high-level security strategy
  
  

This rising comfort level contrasts sharply with the lack of governance in place. Organizations are implementing agentic AI for sophisticated, business-critical tasks, but without the guardrails needed to keep these systems safe. For example, fewer than half of organizations (48%) have developed frameworks to limit or define agent autonomy, including where agents can operate, what data they can access, and what actions they can take. 

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A poorly scoped or misconfigured agent can create serious security risks, like locking employees or customers out of systems, incorrectly modifying access control, or leaking sensitive data. Without these foundational components, agents can take actions that are technically “correct” according to system logic but misaligned with policy, business intent, or regulatory expectations.

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Leaders recognize this: 62% fear that mismanaged agentic AI could erode customer trust. With regulators watching closely and AI governance frameworks evolving, the organizations that address this lack of control now will be better prepared to demonstrate transparency, safety, and accountability as expectations continue to rise.

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Using agentic AI effectively: The security use cases that build trust

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Agentic AI can meaningfully strengthen a company’s security posture when it’s applied to the right problems and supported with strong oversight. Agents excel at tasks that rely on comparing well-defined rules against constantly shifting data.

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Khush Kashyap, Senior Director of GRC at Vanta, emphasizes that the most impactful security use cases fall into four core areas:

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1. Security operations assistance: AI agents can summarize alerts, enrich indicators of compromise, identify patterns across large data sets, and suggest next steps.

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2. Threat detection and prioritization: Agents can triage vulnerabilities, detect anomalies, identify phishing and deepfake attempts, and feed context directly into patch pipelines.

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3. Identity and access governance for non-human users: As machine identities grow, agents are increasingly valuable in monitoring secrets, tokens, and least-privilege access.

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4. Secure coding and live-environment checks: Agents can support developers by running code reviews, enforcing policy checks, and providing real-time insights in live environments.

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Still, none of this replaces human expertise. 

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“Humans need to design the workflows, define the scope, and validate every output. The human aspects don’t go away just because AI is involved.”
— Khush Kashyap, Senior Director of GRC, Vanta

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While some organizations are already adopting these use cases, many are still bogged down by repetitive, manual security tasks like repeated evidence collection and manual access governance reviews. Agentic AI can ease this burden, as Khush notes, but safe adoption requires intentional structure.

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The governance practices that make agentic AI safer

To deploy agentic AI in ways that strengthen security and build customer trust, organizations need a clear governance foundation. These are the practices every security team should prioritize.

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1. Define scope and guardrails before deployment

Agentic AI must operate within clearly defined boundaries. Before rolling out any agent, security and IT teams should decide which assets it can access, what data it can process, when human approval is needed, and how actions will be reviewed or rolled back. They should also establish escalation paths for any ambiguous or unexpected behavior.

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Khush says this upfront work is essential: Organizations must be thoughtful about the safeguards they implement across people, processes, and technology for agents to behave as intended.

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2. Maintain continuous human oversight

Even well-designed workflows require ongoing governance. Teams should monitor agent activity in real time, validate recommendations, maintain auditable logs for every action, and regularly review performance for accuracy and safety. Clear lines of accountability help ensure that high-impact decisions remain human-led.

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Human oversight is especially important in areas that involve judgment, customer data, or irreversible changes.

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3. Automate the right workloads and nothing more

Agentic AI is most effective when applied to repetitive, low-value tasks that consume significant team bandwidth, such as evidence collection, access governance reviews, high-volume monitoring, or time-sensitive alert enrichment. These are ideal areas to offload to agents.

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Trust in agentic AI starts with strong governance

The bottom line is that agentic AI can help teams move faster, while maintaining customer trust, but only when organizations pair automation with strong governance, transparent controls, and continuous oversight.

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Vanta’s Agentic Trust Platform helps teams embed human review, policy enforcement, and continuous monitoring into every agentic workflow, so organizations can innovate fast without increasing risk.

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