The Need for an AI Risk Review To Navigate the New Normal

Organizations are moving fast. Generative AI, media generators and code assistants all are already appearing in everyday workflows. Agentic AI, agents that can reason, plan and execute multi-step tasks as a replacement to existing processes are following close behind. Both promise dramatic productivity gains, but they also introduce new categories of risk that traditional IT governance frameworks were never designed to handle.

As you draft acceptable-use policies, roll out enterprise licenses or let agents touch production systems, don’t let the speed of change distract you from recognizing the risks these new systems bring in. This is not compliance; this is a review that should expose the real risks your organization will face as generative and agentic AI are introduced into your operations.

The two dimensions that stand out as especially urgent for almost every organization are user acceptance and data governance.

Why an Audit First?

Defining governance without understanding your risks is like writing the rules of a game you are already playing. You only spot the problems once it’s too late. By then, employees are already using shadow tools, sensitive data may already be exposed or an agent has taken an action no human would have signed off on.

An AI Risk Review answers three practical questions:

• What exactly are we exposing the organization to?
• Where do the highest-impact risks sit for our users and our data?
• What controls, processes and cultural guardrails will actually work here?

You should define what AI framework you will work with (whether that is NIST AI RMF, ISO/IEC 42001 or both) and, if you touch the European market, how to adhere to the EU AI Act.

The audit should address both AI categories you could be deploying: generative systems that create new content and agentic systems that act on your behalf.

Generative AI (New Content Now With Even More Risk)

Generative AI User Acceptance Risks

As employees begin to use it, they should quickly realize some truths about AI. It can sound brilliant while being completely wrong. It can slip into bias.

They typically respond in two ways:

• Some people write it off as useless.
• Others trust it so much they copy and use whatever it spits out without worrying about it.

Both paths create problems. If you do not define how it should be used, your people will define it for you, and not always in ways you can control or would condone.

Generative AI Data Risks

Unless your contact and settings explicitly block model training, intellectual property, customer PII or proprietary strategy can leak into public models. Depending on the deployment model and vendors’ controls (even in “private” enterprise instances), the outputs may inadvertently reveal to internal users privileged information, training data or patterns that shouldn’t be exposed to employees that should not have access. You also need to understand your regulatory risks and how the use of AI fits into those frameworks.

Data-flow paths noting which tools store prompts, how long they are retained, what data the AI is exposed to and whether they cross access boundaries should all be mapped.

Agentic AI (Autonomy Amplifies Every Risk)

Agentic systems take the next leap. They don’t just generate text; they decide, call APIs, query databases, take action and update records. That autonomy multiplies both user-acceptance and data-handling exposures. It is acting on your company’s behalf after all.

Agentic AI User-Acceptance Risks

We are already seeing agentic AI in critical decision-making chains, such as autonomous agents being used to triage patients, approve or deny loans, reconfigure industrial control systems or define hiring, promotions or terminations with autonomy or very limited human sign‑off.

Accountability blurs and the loss of control amplifies as responsibility shifts to opaque algorithms. These errors can cause irreversible damage. Your deployment most likely will not be this high-risk, but you should still implement controls to address key factors:

• When should the agent escalate?
• Who has the authority to override it?
• Are your teams ready to treat an agent’s output as guidance rather than a concrete decision?

Agentic AI Data-Handling Risks

Agents are data conduits by design. They read from one system, reason, then write to another. A single compromised or misconfigured agent can exfiltrate records across silos, grant excessive permissions, create persistent back doors or contaminate data within multiple systems through a single event.

You should know the entire data journey an agent interacts with, from initial inputs through internal memory and tool integrations to the final output. Be sure to understand its consent mechanisms (for example, whether the agent can recognize when it is handling regulated data), define encryption standards in transit and at rest and audit-log completeness. Because agents can chain actions across days or weeks, also confirm retention policies and “forgetting” capabilities that generative tools rarely require.

Why This Matters

Generative and agentic AI are a transformative technology; they require a well-defined plan to deploy safely. Treating them as standard applications creates blind spots that lead to user revolt, data loss, compliance failures and strategic errors. Understanding user‑acceptance and data‑handling risks up front gives you the visibility and legitimacy to design governance that truly works.

To learn more about AI risk in your unique organization, contact your Warren Averett advisor directly, or ask a member of our team to reach out to you.