Why CISOs Like Me Don’t Sleep in 2025: What You Must Know About Securing GenAI

A frontline CISO perspective on strategically integrating GeAI, the shifting threat landscape, the new accountability risks security leaders face, and how enterprise guardrails must evolve, whether your infrastructure is cloud-native or hybrid.

When I talk to my peers across industries, there’s a shared truth that keeps surfacing: Generative AI is one of the most powerful and most unpredictable technologies we’ve ever had to secure.

As security leaders, we are no longer just defending networks and endpoints. We are now tasked with securing decisions made by non-human agents that interact with our users, influence our operations, and shape our brand. These systems behave dynamically, often without clear auditability or control, presenting a new frontier for risk management.

If you’re a CISO in 2025, you’ve likely engaged in more GenAI-related risk discussions this quarter than you did throughout all of 2023 -2024. I certainly have.

What keeps me up at night isn’t a speculative AGI scenario. It’s the reality that GenAI tools have landed across the enterprise without mature guardrails, and that well-meaning employees can create serious exposure with a single prompt.

We are dealing with a new category of internal risk. It is not based on malicious intent, but it is no less impactful. The models are getting smarter, the systems are getting faster, and the level of internal oversight is often still catching up.

Here’s what I believe CISOs, both in cloud-centric and hybrid environments, need to prioritize.

1. Guarding Against Sensitive Data Leakage in GenAI Workflows

Sensitive data is inherently mobile. When a support agent pastes a customer escalation into a chatbot to draft a reply, or a product manager uses a generative slide builder with internal roadmap content, sensitive inputs are leaving our environment. These actions feel routine, but the consequences are anything but.

The problem is that most of these GenAI tools function as “black boxes”. We do not know if prompts are stored, logged, or reused. In some cases, it is not clear whether inputs are being incorporated into future model training. This is data leakage without any of the usual telemetry or traceability.

We have focused on updating our Data Loss Prevention (DLP) strategy, but even more important is empowering employees to understand what types of information cannot be shared with external systems. This includes building policies that are easy to follow and offering toolsets that default to safe behaviors by design.

2. Addressing Shadow AI Across the Enterprise

Just like we saw with Shadow IT, we are now seeing widespread adoption of GenAI tools outside official procurement and governance channels. Legal teams are drafting with assistants, marketers are generating assets, and sales teams are running CRM notes through chatbots. Many of these tools never touch our monitored infrastructure.

The scale and speed of this adoption have made detection incredibly difficult. Most tools require only a browser and an internet connection. There is no install, no login, and often no centralized oversight. Security teams are operating without visibility into what’s being used, by whom, and for what purpose.

Our response has been to meet teams where they are. We offer clear guidance, establish a review process for GenAI tools, and provide secure alternatives that fit both cloud and on-premise needs. Blocking alone does not scale. What works is giving teams safer, supported paths to innovate within controlled environments. Blanket blocking isn’t scalable. What truly works is enabling teams to innovate safely within controlled environments.

3. Establishing Robust AI Data Governance

Inside most organizations, there is still no single owner of AI governance. Questions like who is responsible for data quality, who reviews outputs for compliance, and who handles downstream liability are still unanswered.

But when something goes wrong, whether it is a hallucinated price recommendation or an output that discloses internal codenames, the first call is to security. The accountability lands here, even if the deployment didn’t.

We are building a cross-functional governance structure that includes legal, compliance, engineering, and security. It starts with mapping AI use cases, classifying risk by exposure level, and defining review and escalation processes. It does not have to be perfect from day one, but waiting is no longer an option.

4. Enhancing GenAI Incident Investigation Tools

When a model behaves unexpectedly, whether it generates offensive content, outputs biased language, or exposes confidential terms, we are often left asking the same question: What exactly caused this?

“Traditional” incident response relies on determinism. GenAI does not. A single prompt may yield different results depending on prior context, temperature settings, or model versioning. When you add retrieval-augmented workflows and post-processing layers, the investigation surface becomes opaque.

This lack of explainability makes it difficult to satisfy audit requirements or support compliance obligations. We are often flying blind when we need to be at our sharpest.

We are responding by logging prompt and output pairs, capturing metadata like model versions and system messages, and requiring all internal GenAI projects to include runtime observability. Over time, we are pushing for tools that give security teams direct visibility without requiring deep model expertise.

5. What we’re doing at ActiveFence: Proactive Misuse Prevention and Response

The risks I’ve described so far are about employees using GenAI tools in ways that introduce unintentional exposure. But there’s another class of risk we take very seriously, and that’s the misuse of GenAI applications by malicious actors.

Even when our tools are well-configured and our employees follow policy, bad actors can exploit GenAI systems to produce harmful outputs, override filters, or extract unintended information. These threats look more like abuse than accidents, and require a different layer of defense.

At ActiveFence, we’ve built a dual approach to manage these safety risks:

• Our red teaming solution puts GenAI systems through adversarial testing using real-world abuse tactics, across modalities and languages. We simulate prompt injection, jailbreaks, impersonation attempts, and other misuse paths that a motivated attacker might try.
• We also deploy real-time guardrails that sit in the runtime environment of GenAI applications. These guardrails detect and respond to unsafe or misaligned outputs as they happen, aligned to the specific policies and risk thresholds of the organization. We focus on detection, observability, and automated response at the session and user level, not just blunt content filters.

We treat GenAI safety as an ongoing, operational problem, not a one-time test. The goal is not just to block bad outcomes, but to understand how and why they’re happening, and to improve over time.

Final Considerations

CISOs are being asked to greenlight GenAI projects, validate model behavior, and respond to incidents in environments we often did not architect and cannot fully explain. That is a difficult place to lead from.

But it is the role we have. Static controls and once-a-year audits are not enough. We need systems that observe, adapt, and enforce in real time. We need partnerships with engineering and legal that are built around shared responsibility. And we need policies that reflect how GenAI is actually used inside the enterprise, not how we wish it were used.

And most of all, we need to stay engaged, not just as gatekeepers, but as partners in enabling safe, secure, and accountable AI adoption.

—

Want visibility into your AI agents before they make the headlines? ActiveFence offers solutions for GenAI governance, observability, and safety.
Learn how we help security leaders enforce policy without slowing innovation. Book a demo today.