Zero Trust for AI: Extending “Never Trust, Always Verify”
▼ Summary
– AI agents expand organizational attack surfaces by making autonomous decisions and accessing sensitive data at machine speed.
– Current security frameworks are inadequate for AI agents, requiring Zero Trust principles to be extended to these non-human actors.
– AI agents must be treated as first-class identities with unique credentials, least-privilege access, and continuous monitoring.
– Excessive agency occurs when AI agents have overly broad permissions, risking unintended harmful actions like data breaches or system manipulation.
– Secure AI deployment requires scoped credentials, tiered trust models, access boundaries, and clear human ownership to balance innovation with safety.
Businesses are quickly integrating AI assistants and autonomous agents into their daily operations to enhance productivity and streamline complex tasks. However, this rapid adoption introduces significant new security vulnerabilities that many existing frameworks fail to address. AI agents operate with a level of autonomy that traditional security models simply weren’t designed to handle, making it essential to extend proven principles like Zero Trust to cover these non-human actors.
For a long time, identity management focused primarily on human users. Over the years, this expanded to include service accounts, containers, and APIs, often referred to as machine identities. Today, a new category is emerging: agentic identities. These AI systems can learn, adapt, and make independent decisions, functioning with human-like flexibility but at machine speed. This dynamic behavior makes their actions harder to predict and their access needs constantly changing.
Despite their advanced capabilities, many AI agents still run on hard-coded credentials, possess excessive permissions, and operate without clear accountability. It’s comparable to giving an intern unrestricted administrative access and encouraging them to act quickly. For Chief Information Security Officers (CISOs) aiming to deploy AI safely, these agents must be treated as first-class identities, subject to even stricter governance than human employees or conventional applications.
The core idea behind Zero Trust, “never trust, always verify”, applies perfectly to autonomous AI. This approach assumes breaches will occur and requires every access request, regardless of origin, to be authenticated, authorized, and continuously monitored. Implementing this for AI involves several key practices.
Every AI agent must have a unique, auditable identity. Shared credentials or anonymous tokens are unacceptable; each action an agent takes should be traceable back to its specific identity. Adopting a least-privilege model by default ensures agents receive only the minimum access necessary to perform their designated functions. For example, an agent programmed to read sales data should not have permissions to alter billing records or enter HR systems.
Because AI agents evolve, their permissions cannot remain static. Dynamic, contextual enforcement means continuously reassessing access rights as tasks and environments change. Real-time context, such as what data is being accessed, by which agent, and under what conditions, should drive authorization decisions. Additionally, continuous monitoring and validation are non-negotiable. Autonomous does not mean unsupervised. Unusual activities, like accessing unfamiliar systems or transferring large data volumes, should trigger immediate alerts or interventions.
While AI is adopted to foster innovation and efficiency, it does not intend harm, but it can still cause it. Consider a helpdesk agent with broad system access. A simple misconfiguration or malicious prompt could lead it to reset passwords, delete records, or send confidential information outside the organization. These aren’t just hypotheticals; such incidents are occurring. AI agents can hallucinate, misinterpret instructions, or operate outside their intended scope. Attackers are actively targeting these weaknesses, exploiting what can be termed Excessive Agency, when AI systems are granted more power than necessary without adequate safeguards.
Security teams now face the challenge of enabling innovation while maintaining control. The key is building scalable guardrails that don’t create bottlenecks. One effective strategy involves using scoped tokens and short-lived credentials instead of permanent secrets. These time-limited tokens have narrowly defined permissions and, if compromised, expire quickly to limit potential damage.
Implementing a tiered trust model allows low-risk, routine tasks to proceed automatically, while high-risk actions, such as deleting data or transferring funds, require human approval or multi-factor authentication. Establishing strict access boundaries prevents agents from interacting with systems or services outside their designated scope. Finally, every agent should have a clear human owner responsible for its behavior, purpose, and permissions.
We are entering an era where logins and digital interactions are no longer exclusive to people. AI agents are coding, analyzing data, managing risks, and interacting with customers. Treating them as secondary in identity strategies means building on blind trust, the very thing Zero Trust aims to eliminate. CISOs must take the lead by explicitly incorporating autonomous agents into their Zero Trust frameworks. This requires investing in identity-first security architectures, specialized monitoring tools, and governance systems capable of managing non-human actors.
Ultimately, security isn’t about hindering AI, it’s about enabling it to operate safely, predictably, and with clear accountability.
(Source: Bleeping Computer)
