When Hidden Flaws Surface: Securing AI at Runtime

AI chatbots have become the new front door to enterprise services. They resolve customer issues, provide financial guidance, and increasingly power everyday business interactions. With that ubiquity, however, comes an unsettling question that executives cannot afford to ignore:

"Could someone exploit my AI chatbot?"

The reality is yes - and it may already be happening.

Unlike traditional software, AI chatbots don't operate within fixed boundaries. They pull in data from multiple sources and generate responses dynamically. AI-driven chatbots, operating through autonomous agents with tool integration capabilities, increasingly obscure the delineation between access control boundaries. This makes them incredibly useful - but also inherently unpredictable. Every prompt, every response and every agent action creates a potential attack vector. And because outputs cannot be fully predicted, they cannot be fully secured through static predeployment testing.

We've already seen the consequences play out in real life. A dealership's AI bot finalized a contract that sold a car valued at $76,000 for just $1. An airline bot issued a refund that exceeded the original ticket price. Popular consumer chat tools have been tricked into leaking sensitive data. These are not theoretical risks; they are tangible failures that expose businesses to operational, financial and reputational harm.

Static code scans, keyword filters or penetration tests are designed for deterministic systems, but generative AI is different. It evolves in real time, adapts to each input, and pulls data from multiple sources. Attacks such as prompt injection, jailbreaks, denial-of-service, tool misuse and data exfiltration are not quirky one-offs. They are now repeatable attack methods, documented in adversary playbooks and actively used in the wild.

The implications for business are severe. Operational disruptions can lead to costly downtime and inflated cloud expenses. Exposure of sensitive or regulated data brings regulatory penalties. And once customer trust is broken, it is exceedingly difficult to repair. Kiteworks research already warns that gaps in AI governance are creating a "cascade effect" of security risk across enterprises. Boards and regulators are paying attention, and accountability will rest squarely with the organizations deploying these systems.

That is why runtime security has become the new frontline of AI defense. Protecting AI cannot stop at static guardrails or prerelease testing. It requires continuous oversight - monitoring every prompt and response in real time, applying adaptive defenses that evolve with new attack techniques, and simulating adversarial behavior before attackers do.

At Palo Alto Networks, we built Prisma AIRS® with this in mind. The solution inspects AI traffic as it happens, detecting and blocking malicious code, preventing sensitive data leaks and intercepting unsafe URLs before they cause harm. It incorporates adaptive guardrails, multilayered controls and continuous AI red teaming, ensuring that protections evolve alongside threats. Low-latency performance keeps security invisible to the end user, while alignment with frameworks like OWASP's AI Top 10 and the NIST AI Risk Management Framework ensures compliance with industry standards.

For executives, the takeaway is clear: AI's promise cannot be realized if runtime remains a blind spot. Waiting until an incident occurs is no longer an option; the risks are immediate and measurable. By investing in runtime security now, organizations move from uncertainty to confidence, from reactive patching to proactive resilience.

Governance is not a brake on innovation - it is the seatbelt that allows you to accelerate safely. Your chatbot is under attack. The time to defend it is now.

Secure your AI at runtime. Deploy Bravely.