Top 5 Considerations for Effective AI Runtime Protection

AI is quickly becoming the new norm for business innovation. AI apps and agents now power customer and employee experiences and streamline business processes. But as adoption accelerates, security remains a top concern - especially as agents gain access to sensitive data and enterprise resources. This creates a new attack surface that adversaries can exploit to exfiltrate data, trigger unintended actions, and disrupt the business.

Legacy firewall-based systems are not built to protect AI, and though there are numerous up-and-coming security solutions on the market, none of them address the full breadth of threats and are not built for enterprise scale. AI runtime protection, in particular, is a critical piece of a comprehensive security solution. Without effective AI runtime protection, businesses are left exposed to numerous threat vectors that can damage their business and compromise their company and customer data. 

At Zscaler, we help 45% of Fortune 500 companies secure their businesses. Many of our customers are AI innovators. CTOs, CISOs, and CAIOs tell us that while AI is transforming their organizations, securing their AI initiatives remains a top concern. Based on our experience, here are the top five considerations that AI and security professionals should evaluate for effective AI runtime protection:

1. Deep visibility into prompts and responses: AI apps and agents converse with LLMs to process queries. Malicious actors can trigger prompts for unintended responses that can lead to data leaks or unintended actions. Getting visibility into prompts and responses is the first step to securing those interactions.
2. Guardrails that cover the full breadth of AI safety and security risks: The interactions between AI apps and agents are exposed to a variety of threats, including security threats such as prompt injections, malicious code insertion, and jailbreaks. Content safety issues and compliance requirements such as toxic and off-topic prompts, undesired responses, and PII data pose additional risk.
3. Effectiveness of detection and data protection: A high number of false positives can distract from real vulnerabilities, while a high rate of false negatives can increase risk. A guardrails solution needs high accuracy in order to be effective. Further, many off-the shelf open-source based data loss prevention engines are not effective at detecting sensitive information across AI apps and LLMs.
4. Ease of integration and enforcement: AI apps, LLMs, and the data they access are dynamic, continuously learning and evolving. Runtime protection is not a one-time action, but an ongoing process that needs to evolve with your AI apps and infrastructure. For this reason, it needs to integrate seamlessly with your AI app and security infrastructure so it can effectively block threats while reducing management overhead and risk.
5. Audit and compliance: A guardrails solution needs to secure AI apps while maintaining auditable logs for compliance and troubleshooting. While visibility is key, privacy of prompts/responses and data collected to enforce security is also critical so it’s not exposed to third parties. 

Accelerate your AI initiatives with Zero Trust

To help our customers protect their enterprise AI, we introduced Zscaler AI Guard. It is a high-fidelity AI runtime protection solution that secures enterprise AI applications so organizations can adopt AI with confidence. It delivers end-to-end inline visibility and control into prompts and responses across AI apps, agents, and LLMs, along with inline allow/block/coach enforcement to reduce data leakage and policy violations. AI Guard has a broad set of detectors for AI security threats (such as jailbreaks, prompt injection, malicious code), sensitive data leakage (such as PII and source code), and content moderation risks (such as toxicity, off topic, competition). It also supports centralized governance and audit-ready reporting aligned to leading frameworks (including NIST, the EU AI Act, and OWASP Top 10 for LLM apps), integrates with major AI platforms and frameworks, and is designed for privacy.

Zscaler helps more than 8,000 enterprises secure their digital transformation journeys. Zscaler’s own IT team serves as customer zero to enable delivery of our security technologies to customers. Watch this video to learn about how the Zscaler IT team uses AI Guard to enable AI guardrails for AI adoption at Zscaler.

AI Guard is the latest addition to Zscaler’s robust portfolio of AI Security solutions that includes Zscaler AI Red Teaming, Zscaler AI Security Posture Management (AI SPM), and Zscaler Generative AI Security. Tune in to our exclusive upcoming launch Accelerate Your AI Initiatives with Zero Trust to hear about new capabilities and learn more about how to secure your AI.