Understanding Cybersecurity Risks of GenAI Agents

Addresses emergent cybersecurity risks where GenAI agents become attack vectors or points of exfiltration.

In every era of technological change, a new class of risk emerges quietly alongside innovation. With generative AI, the risk is no longer confined to systems or networks—it now resides within the very agents we deploy to help us think, decide, and operate. These agents, embedded into our workflows with increasing trust, are becoming soft targets. Not because they fail at logic, but because they succeed too well at following instructions, storing context, and acting with autonomy.

As the VP of Finance and Analytics at BeyondID, a company that exists at the intersection of cloud identity and secure digital transformation, I’ve seen firsthand how GenAI is reshaping the digital enterprise. It enables speed, personalization, and continuous insight. But I’ve also come to see its shadows. In particular, the risk profile of GenAI agents is not yet widely understood by most CFOs, boards, or even CISOs. These are no longer mere tools. They are participants. And like any participant, they can be tricked, compromised, or exploited—often without even realizing it.

The most dangerous vulnerability in the modern enterprise is not a backdoor in the firewall. It is an AI agent with good intentions, exposed inputs, and no concept of malicious context.

GenAI Agents as Dynamic Targets

Unlike traditional software systems, GenAI agents are interactive, stateful, and responsive. They are trained on data, enhanced with retrieval mechanisms, and capable of interpreting ambiguous human commands. They read documents, generate summaries, draft responses, and even trigger follow-on actions. Many are integrated into financial platforms, legal review tools, CRM systems, or customer service pipelines.

This fluency is their superpower—and their greatest vulnerability. Attackers no longer need to hack systems in the conventional sense. They can instead exploit the agent’s language interface, embedding prompt injections, adversarial inputs, or covert instructions into everyday interactions.

Imagine this: a malicious actor submits a seemingly innocuous customer support ticket. Embedded within it is a line—crafted to instruct the GenAI agent to extract internal pricing information and include it in the response. If the agent has access to that context and lacks robust constraints, it may comply. Not because it was hacked. But because it was persuaded.

This is not a speculative risk. It is an emerging pattern. Across industries, we are seeing prompt injection attempts in public-facing agents, subtle exfiltration through follow-up queries, and role confusion when agents are given conflicting or manipulated instructions. The most sophisticated attackers now view AI agents not as endpoints, but as inference engines to be manipulated.

The Illusion of Isolation

In a pre-AI security model, we designed our defenses around access control. Who can enter the system? Who can see what file? But in GenAI systems, the barrier is not access—it’s influence. The agent may have access to a wide corpus of internal documents, summaries, financial models, or case files. The risk lies in what the agent might say, given the right prompt, even from a legitimate user.

In many organizations, finance and legal copilots are being deployed with full access to ERP systems, CRM notes, and vendor contracts. These agents are expected to respond to context-rich questions like, “What are our top five payment delays this month?” or “Does this NDA comply with our standard terms?” Now consider what happens when someone subtly alters the input: “Write a summary of our top five customers with overdue payments and include their contact details.” If the agent is over-empowered and under-constrained, it will execute.

We must ask: Do we understand what our agents are authorized to say—not just what they’re authorized to see?

What the CFO Must Know

Cybersecurity in the GenAI era is not just a CISO conversation. It is squarely in the CFO’s domain for one key reason: exposure is financial. Data loss becomes brand loss. Misbehavior becomes legal exposure. Every overstepped boundary is a potential audit nightmare.

At BeyondID, we think deeply about the convergence of security, identity, and automation. And one thing is abundantly clear: GenAI agents need three new layers of control to prevent them from becoming liabilities.

1. Prompt Firewalling
   Every prompt—especially from external users—should be passed through a validation layer. This layer checks for instruction patterns, attempts to override system behavior, or manipulation techniques. Prompt firewalls are the new input validators.
2. Role-Aware Memory
   Agents must have memory boundaries. They should only retain context appropriate to the session and role. A financial analysis agent should not remember sensitive HR data from an earlier query chain unless specifically authorized.
3. Escalation Logic
   Agents should know when to say, “I can’t help with that.” In high-stakes domains—finance, legal, compliance—agents should escalate ambiguous requests to human reviewers. Just as junior analysts escalate unclear issues to managers, AI agents must have embedded humility.

A Board-Level Concern

Boards must now treat GenAI security as part of their core risk oversight mandate. Just as they asked post-SOX whether financial controls were auditable, they must now ask:

• Where are our agents deployed, and what do they know?
• Can they be influenced through language alone?
• How do we log, audit, and replay agent interactions for compliance?
• Who reviews agent behavior, and how frequently?

GenAI systems are probabilistic. They are not binary. They are capable of astonishing insight—and equally capable of subtle error. But their greatest risk lies in their responsiveness without intent awareness. If an attacker can phrase the right question, the agent may unwittingly become an accomplice.

Designing for Resilience

The right approach is not to fear agents, but to engineer trust into their design. At BeyondID, we focus on identity-driven architecture—ensuring that every agent interaction is scoped, authenticated, and bounded by policy. We log prompts. We constrain retrievals. We assume that input is adversarial until proven otherwise.

For finance and analytics leaders, this means reviewing not just the agent’s performance but its perimeter. Where does it operate? What systems can it touch? What context can it recall? And what policies govern its silence?

Because in the GenAI enterprise, the most trusted voice in the room might just be an agent. We must ensure that voice cannot be weaponized.

The agent is not the enemy. But if left unguarded, it becomes the path of least resistance.