Most organizations never planned for AI to start making real decisions. They started with simple helpers. An agent answered basic questions or generated small automations so teams could avoid opening another IT ticket. It felt harmless.
But as these agents become more capable and more autonomous, they begin operating across systems at machine speed. They connect tools, provision access, and trigger chained actions long after the original request. Eventually, every incident review raises a deceptively simple question: Was this a person, or was it an agent acting like one?
That uncertainty exposes a bigger issue. AI agents are becoming operational identities, and many organizations are not ready for what comes with that shift.
When AI becomes self-serve, security must adapt
Democratization is no longer theoretical. Today, anyone with the right prompt can build an automation, generate analysis, or connect data in ways that once required specialized expertise. This is powerful and necessary. It’s also a fundamental change in how work happens.
Humans have built-in circuit breakers. People pause when something looks off. They understand surrounding context. They improvise.
Agents do not. They execute literally, continuously, and without situational awareness. When something unexpected happens, they cannot learn from it unless someone teaches them. And when something goes wrong, organizations need far more clarity than most realize.
To move quickly without losing control, companies need a security model that matches the speed and autonomy of AI.
Every AI agent is now an identity
In the traditional model, an application might have a few service accounts or API keys. AI has completely reshaped this reality. Each agent now carries:
- Its own identity
- Permissions granted on behalf of a user
- Access to systems that it can invoke or chain together
- The ability to trigger additional agents
This creates identity entropy. When identities multiply faster than guardrails, visibility declines, and accountability becomes blurry. Questions like who acted, why it happened, or how fast it occurred become surprisingly difficult to answer.
That’s why identity security has become the center of gravity for managing AI agents at scale. Identity-centric controls can help to solve this problem. They transform agents from unpredictable actors into governed participants.
Identity guardrails make AI usable at enterprise scale
Many believe controls slow innovation. In practice, identity is the mechanism that makes AI scalable, predictable, and safe. Identity guardrails allow organizations to:
- Apply just-in-time (JIT) access instead of standing privileges
- Enforce least privilege for people and agents
- Trace actions with precision
- Set rate limits before an agent overwhelms a system
When each agent has a clearly defined identity, you can decide what it can do, when it can do it, and how quickly it can operate. If something misbehaves, you can intervene surgically instead of disrupting entire workflows. This is the difference between responsible adoption and experimentation that never reaches production.
Modern phishing has become an authentication challenge
AI-driven phishing is now highly convincing, fast to generate, and spread across multiple channels. Attackers increasingly impersonate executives through text, voice, video, and social messaging. By the time something feels suspicious, trust has already been established.
Training remains important. However, training alone cannot keep pace.
Organizations need phishing-resistant authentication that eliminates entire categories of risk. FIDO-compliant methods ensure that stolen or intercepted credentials cannot be reused, even if a user makes a mistake.
This is the only reliable way to counter modern, AI-enabled social engineering.
Citizen-built automation needs an identity-first structure
AI has made it easy for anyone to build applications and automations. This creativity is valuable and often accelerates innovation. It also introduces operational risk when guardrails are unclear. A structured approach protects both the environment and the innovators inside it:
- Choose a controlled ecosystem instead of a scattered mix of tools.
- Start with a small group of users and expand as you learn.
- Apply identity-based permissions to every automation.
- Enforce rate limits and scope restrictions from the beginning.
Democratization should increase productivity, not create accidental outages. Identity plays a key role in keeping what users build observable and reversible.
Measure AI by outcomes, not usage
Many organizations adopt AI because competitors are doing so as well. That pressure is understandable, but it does not create value. Meaningful measures of success include:
- Time saved on specific tasks
- Reduced delays and handoffs
- Improved time to market
- Clearer and faster decision paths
Short contracts and strong documentation allow teams to adapt as tools evolve. The underlying ideas and workflows should persist even if the platform changes. This is how organizations avoid being locked into a tool simply because switching feels inconvenient.
Identity is the control plane for AI’s future
AI agents will continue to become more autonomous and more embedded in enterprise operations. The organizations that treat identity as the foundation for both people and machines will make faster, more confident progress. They will understand what happened when incidents occur and respond with clarity rather than guesswork.
No environment is perfect. Resilience comes from knowing who or what is acting at any given moment and having the controls to respond without stopping the business.
Ariel Pisetzky is the chief information officer at CyberArk.
🎧 Listen now: Want the full story behind how AI agents are reshaping identity, risk, and enterprise operations, and why FOMO (yes, fear of missing out) is pushing some organizations into dangerous territory? Hear Ariel Pisetzky expand on the risks, identity challenges, and the guardrails that matter most in his recent Security Matters podcast interview. Listen below or find it on major podcast platforms.
