Listen to this Post
The Rise of Autonomous AI — And Its Hidden Dangers
Artificial intelligence has entered a new era of autonomy. From OpenAI’s custom GPTs to enterprise-grade copilots, AI agents now operate like digital teammates — making decisions, executing commands, and even invoking other AI systems on their own. They’re no longer just tools. They’re actors. But with this new independence comes a troubling question that security experts can’t ignore: if AI is working for us, how do we know when to trust it?
Traditional security frameworks, such as Zero Trust architecture, assume no entity can be trusted by default. Every user, device, and service must constantly prove its identity and authorization. Yet in the world of agentic AI, this principle collapses. These new agents often act under inherited credentials, with no registered owner or defined governance. They might look legitimate, but in reality, they could be dangerous — rogue programs operating without oversight.
Ido Shlomo, CTO and Co-Founder of Token Security, explains that to regain control, organizations must apply the NIST AI Risk Management Framework (AI RMF) through a Zero Trust lens centered on identity. Without identity as the core of AI governance, every layer of security — access control, accountability, auditability — becomes unstable.
Identity Risk in the Age of Agentic AI
The NIST AI RMF outlines four key pillars: Map, Measure, Manage, and Govern. When filtered through the lens of identity, each reveals where AI-specific risks emerge.
Map: How many AI agents are currently operating within your network? Who created them? What access do they have to internal systems? For most security teams, the answer is unsettling: they simply don’t know.
In many organizations, AI agents are being created in development environments, production servers, or even employee laptops without formal approval. These “shadow agents” often use long-lived credentials, lack rotation policies, and have no assigned owners.
Measure: Many AI systems inherit far more permissions than they need. Without identity-based controls, these over-permissioned agents become weak points that attackers can exploit.
Manage: Without ongoing review, these agents persist indefinitely, accumulating access privileges and operating far beyond their original scope.
Govern: The absence of clear identity governance creates what experts call orphaned agents — autonomous systems that operate outside the security perimeter. These agents are the antithesis of Zero Trust: entities acting with power, but without proof.
Securing the Future: Identity Before Trust
The solution begins with the first rule of Zero Trust: never trust before verifying. This principle must now extend beyond human users to encompass AI systems.
Every AI agent should have:
A unique, managed identity
A clear owner or team responsible for its behavior
Intent-based permissions that align with its tasks
A defined lifecycle: creation, review, rotation, and retirement
When applied correctly, identity transforms agentic AI from a potential liability into a governable, auditable entity. Every action — from reading data to executing commands — can be traced back to a verified identity.
Applying NIST AI RMF Through Zero Trust
Each stage of the NIST AI RMF can be enforced through an identity-driven Zero Trust strategy:
Map: Discover all active AI agents — from copilots to custom GPTs — and document their owners, access rights, and behaviors. Monitor continuously for anomalies, such as expired credentials still in use or new system interactions that deviate from past patterns.
Manage: Adjust permissions dynamically. Remove unnecessary access, rotate credentials, and deactivate agents that no longer serve a purpose.
Govern: Apply human-grade governance standards to AI. Every action must have a traceable chain of responsibility. When an AI performs a sensitive operation, security teams should know exactly who approved it and why.
From Blind Trust to Proven Accountability
The risks of ignoring identity are severe. Orphaned agents can serve as hidden gateways for cyberattacks. Over-permissioned agents can leak confidential data instantly. And in the aftermath of a breach, organizations without clear identity trails are left with a chilling uncertainty — “We don’t know who did it.”
Identity should not be an optional layer in AI security. It must be the foundation. By ensuring that every AI action is tied to a governed, accountable entity, enterprises can build genuine trust in automation. Autonomous AI doesn’t mean ungoverned AI. It means AI that acts independently, but within the guardrails of verified identity and continuous oversight.
Without this transformation, the industry risks sliding into a future where trust in AI becomes impossible to measure — and too costly to lose.
What Undercode Say:
The evolution of agentic AI represents both innovation and risk. It’s a technological leap that redefines productivity but also tears open traditional security frameworks. The article’s insight into identity-based governance hits at the heart of what cybersecurity will look like in the next decade: AI systems that must prove who they are before they act.
This isn’t just theoretical. In today’s enterprise ecosystems, most organizations already run multiple AI copilots, data bots, and internal GPTs. Many of these operate with privileged credentials — database access, API keys, cloud permissions — yet lack any formal owner. This is the digital equivalent of having employees with full system access but no HR record.
The most pressing issue is visibility. Security teams cannot protect what they can’t see. Mapping AI agents across hybrid and multi-cloud environments is the first defense line. The next step is identity instrumentation — embedding unique identifiers and behavioral analytics into each agent to ensure they act only within approved parameters.
Zero Trust must evolve. It’s no longer about verifying human logins or endpoint devices; it’s about verifying intent. AI identity governance introduces a paradigm shift: permissioning based not just on “who” or “what,” but on why the AI is acting.
Enterprises should begin designing AI identity registries, assigning digital certificates to each agent, and integrating monitoring systems that detect drift from expected patterns. When an agent requests an action outside its defined intent, the system should flag, pause, or quarantine it automatically.
Over the next five years, expect major security vendors to integrate identity-driven AI firewalls, where each AI’s interactions are authenticated, logged, and analyzed for anomalies in real time. This approach will turn the concept of accountability from reactive to proactive.
Ultimately, the question is not whether AI can be trusted, but how trust can be continuously proven. Just as humans undergo compliance audits, AI systems must face the same scrutiny. The organizations that master this alignment between autonomy and identity will be the ones that safely scale AI adoption without fear of unseen breaches.
🔍 Fact Checker Results
✅ AI agents increasingly operate autonomously across enterprise environments.
✅ Lack of identity governance is a major, documented cybersecurity risk.
✅ The NIST AI Risk Management Framework (AI RMF) recommends governance models compatible with Zero Trust principles.
📊 Prediction
🔮 Within three years, identity-driven AI governance will become a regulatory requirement for enterprise compliance.
⚙️ Expect to see the rise of “AI identity orchestration” platforms that track and validate every autonomous agent.
🧠 Organizations that adopt Zero Trust for AI early will gain a significant edge in both security resilience and customer trust.
🕵️📝✔️Let’s dive deep and fact‑check.
References:
Reported By: www.bleepingcomputer.com
Extra Source Hub (Possible Sources for article):
https://www.discord.com
Wikipedia
OpenAi & Undercode AI
Image Source:
Unsplash
Undercode AI DI v2
Bing
🔐JOIN OUR CYBER WORLD [ CVE News • HackMonitor • UndercodeNews ]
📢 Follow UndercodeNews & Stay Tuned:
𝕏 formerly Twitter 🐦 | @ Threads | 🔗 Linkedin | 🦋BlueSky | 🐘Mastodon
