Accelerated AI adoption is ushering in a new security risk that is quietly taking root in enterprise environments. Shadow AI Agents have many similarities to the risks stemming from Bring Your Own Device (BYOD), where unsanctioned devices in the workplace introduce unmanaged risks. Now, instead of mobile devices, AI agents are entering organizations under the radar, often deployed by employees and departments with minimal to no oversight from IT or security.
For those skeptical of the impact an autonomous AI agent could have on their business, this is for you. Autonomous AI agents are powerful and capable, and in many instances, they run outside traditional identity governance frameworks. If left to their own devices, they represent one of the most significant emerging insider threats enterprises face today.
Why Shadow AI Is the Next BYOD
The barrier to deploying AI agents has never been lower. That’s because employees can now easily spin up autonomous agents using cloud APIs, marketplaces, or no-code platforms in minutes. As mentioned earlier, this can all be done under the security team’s radar. Once live, these agents begin analyzing data and/or answering questions. However, there’s more: agents can also initiate workflows, chain tasks, and make autonomous decisions.
This autonomy is as valuable as it’s frightening. These are the traditional bots we know well, programmed to follow static instructions. Agentic AI bots interpret intent, interact dynamically with APIs, and take initiative, all without human oversight. In many ways, it’s safe to say that they operate like employees, but without the same levels of accountability, oversight, or identity controls.
The Inherent Risks of Agentic Systems
It’s the autonomy and unfettered nature that should concern any business that has or plans to deploy AI agents. These agents can create sub-agents, grant permissions, and chain processes together. Every time this occurs, they open the business to new exploits that don’t include traditional audit trails.
Here’s an example:
An AI agent that has been integrated into a company’s financial application might autonomously initiate transfers, process invoices, or modify vendor records, all in service of perceived business goals. But what happens when that agent is compromised through prompt injection, communication poisoning, or model manipulation? Without guardrails, the blast radius extends far beyond a single task.
Adding insult to injury, these agents often operate with API keys or static credentials, allowing them to retain access to sensitive systems well beyond the intended timeframe. Combine that with a lack of proper monitoring, and these permissions will remain active and exploitable for extended periods.
Common Exploits in the Age of AI Agents
Naturally, these risks I’ve touched on are well known to attackers who are taking action through a variety of tactics, including:
- Prompt Injection: Attackers craft deceptive inputs to manipulate the AI agent’s behavior and enable malicious outputs or actions.
- Agent Spoofing: In this instance, cyber attackers impersonate trusted AI agents to gain unauthorized access and take fraudulent action.
- Communication Poisoning: Here, the bad actors inject misleading, malicious, or manipulated information that agents rely on, which can lead to flawed decisions or security lapses.
What amplifies these scenarios is that they are happening right now, unbeknownst to organizations that lack visibility into these attack surfaces due to their reliance on Identity and Access Management (IAM) systems originally designed to track static, task-specific, and deterministic bots or machine identities, rather than autonomous, rapidly evolving AI agents.
The Shortcomings of Legacy IAM
This issue is about to explode. Gartner reports that by 2025, 80% of companies will have adopted or plan to adopt AI-powered chatbots to support their customer service operations. Those businesses relying on traditional IAM to protect themselves will inevitably learn a valuable, yet potentially costly, lesson. Traditional IAM solutions excel at managing human users and long-lived service accounts. Still, they are no match for autonomous agents that can dynamically spin up, interact across systems, and make decisions independently.
These AI identities are ephemeral, context-dependent, and often lack clear ownership. The challenge is exacerbated by the fact that they don’t fit into existing HR systems, identity provider directory structures, or role-based access models. As a result, they slip through the cracks of current controls, creating a shadow identity layer that security teams can’t see—let alone secure.
It’s Time for Runtime Identity Governance
To gain control over AI agents, companies are rethinking their approach to identity governance, abandoning static controls and periodic reviews, which don’t even scratch the surface. The answer lies in adopting a security approach that monitors, validates, and enforces identity and access decisions in real time.
Aragon Research released a report defining a new category: Agentic Identity and Security Platforms (AISP), designed specifically to meet the unique challenges organizations face when it comes to securing agentic AI identities.
Key Agentic Identity and Security Platforms (AISP) principles include:
- Just-in-Time Access: Access is granted to AI agents only when needed, solely for specific tasks. This access is then immediately revoked when the task is completed, eliminating the risks caused by static privileges.
- Task-Scoped Permissions: Runtime identity governance ensures that all access is explicitly tailored to the agent’s defined purpose and nothing more.
- Continuous Monitoring: Periodic access reviews are swapped out for real-time agent tracking, where all actions are monitored and access can be terminated instantly if anomalous behavior is detected.
- Auditability: In addition to continuous monitoring, all AI identity activity is logged so teams can easily conduct a post-incident analysis and compliance checks.
These measures align with Zero Standing Privileges (ZSP) and Zero Trust principles, offering a path to secure the rapidly evolving landscape of autonomous AI.
The Future of Security: Identities, Not Infrastructure
AI agents are growing fast as organizations seek to realize new efficiency gains. But without proper security guidelines, these new levels of efficiency will be dwarfed by growing vulnerabilities. In this new era, identity is the new perimeter, and that includes AI identities. Those who fail to recognize and address the risks of Shadow AI agents will find themselves vulnerable to insider threats they can’t see, breaches they can’t trace, and attackers exploiting gaps they didn’t know existed.
Rather than blocking these innovations, companies should focus on securing them by treating AI agents as first-class identities. That means enforcing the same runtime governance applied to human users’ dynamic, just-in-time access controls, least privilege, and continuous authorization. This approach allows businesses to embrace the benefits of agentic AI without opening the door to invisible threats.
Art Poghosyan is an entrepreneur and InfoSec expert with over 20 years in cybersecurity. He excels in building high-performance teams and fostering collaborative, accountable cultures. Prior to founding Britive, a pioneering cloud privileged access management (CPAM) platform, he co-founded Advancive, an Identity and Access Management (IAM) consulting firm acquired by Optiv in 2016. Art is a mentor, speaker, and contributor to industry events and (ISC)2 CISSP-ISSAP exam development, deeply committed to advancing cloud security innovations.
The opinions expressed in this post belong to the individual contributors and do not necessarily reflect the views of Information Security Buzz.