AI is no longer experimental. It’s in production—powering chatbots, informing decisions, and reshaping how enterprises operate. But there’s a blind spot hiding in plain sight: inference.
While training grabs headlines, inference is where AI comes to life. It’s where models engage with real users, data, and decisions. It’s also where the most significant security risks arise.
And here’s the reality: most enterprises engage with AI at the inference layer, not during training. That’s where adoption is accelerating—and where security too often lags behind.
From Black Boxes to Attack Surfaces
AI inference isn’t just a deployment phase. It’s a new attack surface—one that’s active, unpredictable, and exposed.
Here’s why:
- Inputs are unpredictable. Every prompt is a potential exploit. Attackers can weaponize seemingly innocent user inputs through prompt injections, jailbreaks, or malicious context chaining.
- Outputs are vulnerable. AI-generated content can leak proprietary data, expose sensitive customer information, or generate toxic or non-compliant responses—all in seconds, and often without detection.
- Models don’t forget. Some language models retain details from their training data. This unintended memorization can result in data exfiltration through casual prompts.
- Behavior is opaque. Foundational models are complex and often behave like black boxes. This unpredictability makes it harder to forecast how they’ll respond in edge cases or while under attack.
- Security teams lack control. Whether using APIs from model providers or hosting open-source models in-house, most enterprises don’t have fine-grained tools to enforce policy, monitor activity, or block attacks in real time.
The bottom line? Inference is not passive. It’s dynamic, continuous, and exposed to live user inputs—making it a prime target for adversaries and a critical gap in most security programs.
The Inference Perimeter: A New Layer of Enterprise AI Protection
Securing AI at inference isn’t a matter of a single control or tool—it requires a layered, adaptive perimeter built specifically for how AI systems are used in the real world.
That begins with enforcing policy at the point of interaction. Enterprises need mechanisms to inspect every prompt and every response—evaluating for prompt injection, harmful content, data leakage, and other context-specific risks. These defensive controls must be dynamic, updating in response to emerging threats and evolving compliance requirements, and all without disrupting business operations.
But securing inference isn’t just about reacting—it’s also about anticipating. As attackers develop novel strategies to exploit model behavior, organizations need offensive capabilities that red-team AI systems. Using advanced adversarial techniques designed for AI ensures that defenses remain ahead of what’s possible, not just what’s known.
Finally, organizations need visibility. Without insight into how AI is being used—what models are being accessed, by whom, for what purpose—there’s no way to govern AI usage effectively. Observability across the inference layer is foundational to both security and trust.
Taken together, these capabilities form a new kind of security perimeter—purpose-built for inference, and essential for any enterprise scaling GenAI to do so with confidence.
From Insight to Action
Inference security is no longer a nice-to-have, it’s a strategic imperative. For security leaders in any organization, the question is no longer if inference should be secured. It’s how soon.
Want to go deeper? Download our white paper, Security Risks of GenAI Inference, to explore the technical risks, deployment trade-offs, and real-world security strategies.
