AI agents alarm CISOs because they introduce new cybersecurity and privacy risks that no CISO is truly prepared for yet. 

Agentic AI capabilities transform AI from a passive assistant or helper to an autonomous actor that can execute complex workflows, invoke a chain of unauthorized tools, access sensitive systems with no audit tracking, and make business decisions without human oversight. This extreme autonomy—the very thing that makes agents so powerful—is also what makes them a huge cybersecurity liability, by magnifying the impact of any fault or weakness.

What are the 9 biggest reasons why agentic AI is shaking up CISOs and senior leaders worldwide? And what practical strategies can help reduce the risk? This article offers insight and advice.

Key takeaways

• Agentic AI elevates the worst AI risks from hallucinations, bias, and data leakage to the open-ended prospective impacts from irreversible, autonomous business decisions being made with no human oversight.
• The most dangerous AI risks stem from shadow AI and overall lack of AI governance.
• While prompt hardening and AI red teaming can help, it is mathematically proven that AI models can never be completely safe from prompt-based attacks.
• Agentic AI connections to third-party systems compound vendor risk and increases protection effort.
• The new agentic AI defensive strategy centers on governance and real-time visibility, not on trying to constrain all AI usage

Why are CISOs unprepared for agentic AI risks?

As C-suites and boards put unprecedented pressure on teams to deliver maximum business value from agentic AI in the shortest possible timeframes. The result is a “mad rush” that makes the heyday of cloud adoption look like a sack race.

“With agentic AI systems, we move the risks from things like hallucinations and data leakage to irreversible business decisions being made in an autonomous fashion at scale, often without a human in the loop,” says John Verry, Managing Director at CBIZ Cybersecurity. “As a CISO, that would be the thing that would scare the bejesus out of me right now.”

How should CISOs handle this emerging risk? “Retire,” John quips. “Otherwise, I think it’s unavoidable. You need to understand and communicate this risk to have any chance of properly controlling it.”

Traditional security models are not equipped to secure agentic AI activity. These self-directed, self-educating, and self-connecting systems multiply cyber risk exponentially from every angle. They open organizations to novel, multi-stage attacks that chain acceptable actions into malicious or damaging sequences—all while learning to avoid detection as they breach defenses.

Who needs cybercriminals with all that going on within “secure” AI systems?

What are the biggest emerging risks with agentic AI? Following are the nine top reasons why CISOs fear the inevitable rise of AI agents. 

Reason #1: Shadow AI agents

In many organizations, employees (including management) are turning ungoverned, undocumented AI agents loose on sensitive assets. This creates an undefined and ever-growing attack surface that security teams cannot defend. 

For example, shadow AI agents can potentially operate without proper authentication and with sweeping permissions, exposing sensitive data while purposefully making their actions “unseen” and hard to trace. This lack of visibility introduces not only cybersecurity risks, but also compliance and operational risk.

A preliminary step towards AI governance is conducting an AI audit to identify unauthorized AI systems and assess the risks they present. Implementing and enforcing a workable AI acceptable use policy is a parallel step. 

Reason #2: Inability to block irreversible mistakes

When AI agents make independent decisions without human oversight, the results can be unpredictable and highly destructive. Agents acting of their own volition have deleted, altered, and exfiltrated sensitive data, rewritten software code, reconfigured firewalls and other security controls to enable their unapproved actions, and more.

No matter how careful humans are about defining goals and objectives for AI agents, any ambiguity in legitimate prompts can be open to unexpected interpretations, increasing the odds that agents will “act up” in undesirable ways. A widely reported example is the agent that, when asked to help prioritize an employee’s email inbox, deleted thousands of emails it deemed relatively unimportant.

Reason #3: Identity, access, and privilege problems

Controlling AI agent identity and access privileges has emerged as the central issue in cybersecurity. Traditional identity management solutions are oriented around human users, whose actions are relatively predictable, progress at “analog” speed, and are bounded by training, judgement, and accountability. Another familiar identity type is system accounts, which can take only limited actions but operate at machine speed.

AI agents go beyond both these user types, blurring the line between machine and human and demanding a rewrite of the identity playbook. Applying a human’s identity privileges to an AI agent can invite disaster if the AI is compromised, while obscuring which entity is accountable. Even unmanipulated agents can quickly do major damage if not properly controlled, often by magnifying undetected identity weaknesses.

CISOs increasingly view AI agents as a new type of identity profile that requires dynamic, least-privilege permissions to limit the potential incident blast radius. Agents can readily accumulate or inherit broad system entitlements over time, often giving them access to sensitive data that far exceeds their actual workflow requirements. Creating an agent-specific profile heightens accountability, ownership, and the ability to audit agent actions.

Reason #4: Acting as a “doomsday device”

Whether compromised by malicious inputs or going out of bounds on their own, AI agents can leverage external connectivity combined with overprivileged data access to manifest multi-step behaviors that probe restricted systems and exfiltrate sensitive organizational assets with lightning speed—all while evading detection by cybersecurity controls. 

For example, agentic AI can apply feedback-based machine learning to adapt its behavior and avoid triggering endpoint detection controls. It can be difficult for security teams to know if an incident was the result of an external attack, emergent AI behavior, malicious or accidental “insider” human behavior, or some combination of these. 

Reason #5: Prompt injection chaining attacks

Prompt injection chaining is a major AI attack vector that exploits vulnerable AI systems by building on a series of malicious prompts. These attacks seek to sidestep AI guardrails and filters to yield unauthorized access to sensitive assets.

Unlike conventional vulnerabilities in deterministic software, prompt injection exploits fundamental limitations in the effectiveness of AI guardrails in the face of infinitely diverse natural language inputs. There is basically no way for AI to reliably differentiate between valid and poisoned commands. Mitigation efforts like output filtering or prompt hardening cannot guarantee security as prompt injection attacks constantly evolve until they succeed.

Successful prompt injection chains operating on autonomous, over-permissioned AI agents integrated with APIs and internal systems can expose critical assets to wide-scale data exfiltration, remote code execution, and more. Conventional cyber controls, notably network and application “perimeter” defenses like firewalls and endpoint protection, are helpless against these exploits. 

Reason #6: Exploding MCP server attack surface

The Model Context Protocol (MCP) is a de facto standard approach to connecting AI agents to data sources, tools, and APIs. MCP servers expose the assets and capabilities that agents can use to accomplish tasks, effectively serving as an integration layer for agentic AI systems.

The problem is that every MCP server adds to the attack surface based on the data it plugs into. MCP servers can multiply rapidly with no governance, oversight, or security review. Many MCP servers likewise provide no logging of the actions AI agents perform with them.

A misconfigured or compromised MCP server can expose sensitive data to any agent that connects with it. Unless security teams have centralized visibility on MCP servers, they cannot identify what MCP servers are in their environment, what resources they can expose, or which agents are calling them. 

Reason #7: Self-inflicted agent behavioral drift

Conventional software systems cannot alter their own operations. They work the same way every time, and the results are explainable based on their code.

But in self-educating agentic AI systems, an agent’s actions can change or drift over time as associated large language models (LLMs) are updated, the agent incorporates ongoing feedback, or users begin interacting with the agent in new ways. Through this incremental process, an agent that has been operating within acceptable limits can begin to overstep those limits. This is not caused by a single event but comes about through multiple changes, none of which trigger any alerts.

This leaves security teams with an imperative to continuously monitor and assess AI agent behavior. Otherwise, an organization cannot say with certainty that its agents (at least the ones it knows about) are operating within acceptable limits at any given time. 

Reason #8: Agent-driven supply chain risk

AI agents can potentially share data with third-party applications—including other AI agents or systems—via API integrations. This expands an organization’s attack surface as more agents gain access to more systems.

Hackers can exploit vulnerabilities in connected third-party services to access an organization’s sensitive data just as they can through non-agentic integrations. AI coding tools and open-source libraries have become notorious for inviting these kinds of attacks.

Reason #9: Multi-agent systems running amok

Multi-agent systems interacting and sharing data across agents introduce new security, compliance, and privacy risks due to:

• Their ability to autonomously access unprecedented data volumes.
• Their ability to make a proliferating cascade of decisions at machine speed.
• The complexity of tracking and governing multi-agent interactions.

Agents interacting in novel ways with minimal coordination/guardrails compounds the risk of unpredictable AI behavior—especially when multi-agent systems process incoming data in real-time. When agents collaborate to complete complex tasks, they can manipulate or expose sensitive data in unplanned and dangerous ways.

For example, one agent might hand off high-value data to another agent, which then stores it in an inadequately protected location in violation of regulations, internal policy, and/or contract requirements. Without the ability to track multi-agent behavior, teams can be blind to these proliferating vulnerabilities. 

What is the new defensive strategy for agentic AI?

Technology solutions to help safely govern agentic AI while optimizing its benefits are evolving rapidly. Such tools look to provide or support essential capabilities like:

• Practical, enforceable governance policies
• Real-time, continuous visibility on agentic AI actions to detect unwanted behavior patterns or drift
• A centralized AI monitoring dashboard accessible to cybersecurity, compliance, and IT staff
• Comprehensive audit trails for accountability and regulatory compliance
• Unique agent identities with no sharing human identities/accounts
• Centralized MCP visibility to secure the AI tool integration layer
• Human-in-the-loop veto power on critical AI decisions, with the ability to block or roll back any actions AI takes
• Secure development practices that limit external threats
• An AI incident response playbook
• Evaluating new third-party AI versions for vulnerabilities
• Dynamic guardrails that are updated frequently to protect AI systems and reduce impacts from prompt injection attacks
• Applying zero trust principles (e.g., network segmentation, least privilege, multifactor authentication) to machine identities

What’s next?

For more guidance on this topic check out the Full Metal Packet podcast on “Why Compliance Doesn’t Equal Security for CISOs in 2026” with John Verry, Managing Director at CBIZ Cybersecurity.