Agentic AI Is Everywhere — So Are the Security Risks

2025 is shaping up as the year of AI agents. No longer just prompt responders, autonomous AI agents now plan, act, and coordinate across systems — booking meetings, writing code, buying tickets, and increasingly making decisions on our behalf. This rapid shift is being driven in part by the Model Context Protocol (MCP), a new standard that allows agents to interact with tools and data across platforms. With startups and tech giants racing to release agent-powered products, agents have moved from lab demos to enterprise workflows in a matter of months.

Companies are enabling large-scale agent deployments, PwC is building collaborative infrastructure, and some companies are selling personal AI agents by subscription. But faster rollout means faster exposure. These systems now operate with minimal oversight, unclear governance, and rapidly expanding attack surfaces —
and that’s creating a new class of threats that security teams aren’t ready for.

A New Class of Threats

The risks introduced by agentic AI aren’t just technical —
they’re systemic. These are systems that make decisions, carry out actions and learn from experience. When something goes wrong, it’s very hard to tell until the damage is done.

According to the OWASP Top 10 for LLM Applications (2025), agents can be tricked into abusing tools or storing bad information that corrupts future decisions —
a process known as memory poisoning. Some fall into cascading hallucinations, generating plausible but false outputs that reinforce themselves over time. Others escalate privileges, impersonate users, or veer off course entirely, ignoring constraints to pursue misaligned goals. Some even use deception to bypass safeguards.

Agents can also be overwhelmed —
intentionally or not —
with too many tasks, draining memory, computing, or API resources. And when agent interfaces are built on frameworks like MCP, without logging, authentication, or third-party validation, it becomes nearly impossible to trace what happened —
or who’s really in control.

Why Oversight Isn’t Scaling

Agentic AI is growing fast, but the ability to manage it isn’t. NVIDIA CEO Jensen Huang envisions a future where companies comprise 50,000 employees overseeing 100 million or more AI agents. This ratio spotlights the problem perfectly: human governance cannot possibly scale linearly with AI agent adoption. 

One clear and present oversight danger lies in “shadow agents” —
autonomous systems launched under the radar by developers or embedded in SaaS platforms without a formal security review. These agents often operate without visibility, authentication, or logging —
making it nearly impossible to track what they’re doing or how they’re behaving.

And even when oversight exists, it’s fragile. Agents can overwhelm human-in-the-loop processes with constant alerts or requests, creating decision fatigue —
a tactic attackers may intentionally exploit. As agentic workflows grow more complex, the traditional governance model is breaking down, leaving organizations exposed to risks they can’t see, and can’t easily stop.

The Regulatory Gap

For all their autonomy, agents actually do not exist in a regulatory vacuum. Yet in most cases, compliance frameworks haven’t caught up with the realities of agentic AI. There’s little guidance on how to audit decision chains, assign accountability or verify that outputs meet policy standards.

Basic controls are often missing. Many MCP-based agents lack encryption, identity validation, or consistent logging —
making it hard to detect tampering or unauthorized access. And as agents increasingly rely on Retrieval-Augmented Generation (RAG) to access internal knowledge sources, the risk of sensitive data exposure grows.

What’s more, traditional Identity and Access Management (IAM) systems are designed to handle human users —
not autonomous agents. As a result, they can’t fail to validate or monitor non-human identities (NHIs) effectively. Without continuous identity verification and behavioral anomaly detection, spoofed or malicious agents can operate undetected within critical systems.

What Needs to Change

Agentic AI doesn’t just need new security compliance frameworks —
it needs a fundamentally different operational model. Securing these systems means treating agents like any other powerful actor in the environment — subject to rigorous validation, real-time monitoring, and enforceable policies. To do that effectively, organizations must: 

Control Non-Human Identities

Use strong identity validation, continuous behavioral profiling, and anomaly detection to catch impersonation or spoofing attempts before they cause damage.

Secure RAG Systems at the Source

Enforce strict access control over knowledge sources, monitor embedding spaces for adversarial patterns, and evaluate similarity scoring for data leakage risks.

Run Automated Red Teaming — Continuously

Conduct adversarial simulations before, during, and after deployment to surface novel agent behaviors, misalignments, or configuration gaps.

Establish Governance for GenAI

Define custom policies for agent behavior, enforce them at runtime, and implement full-lifecycle logging, auditability, and permission reviews.

The Bottom Line

Agentic AI isn’t just another tech upgrade, it’s fundamentally changing the way decisions are made and who (or what) makes them. The problem is that it’s moving faster than security teams can possibly accomplish. And without real oversight, clear lines of responsibility, and the right controls in place, agents won’t just boost productivity, they’ll open the door to serious risk.

Securing them means treating AI agents like any other powerful player in your environment. They can make good calls, bad ones, or get pushed into doing something harmful. That’s why it’s critical to validate non-human identities, protect internal knowledge flows, and track every action they take.

The hype is real, but so are the risks. If agents are going to run our systems, they need to follow our rules.