Runtime Protection Agent
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On-demand Webinar
On-demand Webinar

Risks in AI-Native Systems: Why AI Security Is Still an API Security Problem | Harness Resource | Harness

The shift to AI-native design drastically expands the enterprise API attack surface. Large Language Models (LLMs) and autonomous agents operate via complex, API-chained workflows. This reality of AI system architecture introduces high-velocity, non-deterministic execution paths across your cloud footprint.For security teams, this mandates a strategic pivot: AI security is fundamentally still an API security challenge, but with additional AI uniqueness that can’t be overlooked. AI systems create severe, novel risks around sensitive data exposure, agent identity management, and behavioral anomalies that legacy application security tooling fails to address.In this session, you will learn:How threats such as prompt injection, model misuse, shadow AI and supply-chain poisoning impact AI-native systemsWhy limited visibility and control across the AI and API ecosystem creates significant security riskHow organizations can apply proven API security practices to AI-driven environmentsStrategies for improving AI discovery, testing and protection across AI-native applications.

Published
April 8, 2026

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What you'll learn

Key Takeaways

Shadow AI Poses Immediate Enterprise Risks

A recent survey reveals that 75 percent of organizations already recognize shadow AI as an active threat. The rapid adoption of generative AI and agentic workflows has accelerated this unmanaged technology footprint.

AI Security Remains an API Security Challenge

AI-native systems and autonomous agents rely heavily on complex API-chained workflows to function. Because machines are now making these API calls autonomously, the enterprise attack surface expands exponentially.

Visibility Gaps Lead to Severe Data Leakage

AI systems constantly move sensitive data across a mix of first and third-party services. Without comprehensive visibility into these data flows, organizations struggle to classify information and prevent unauthorized exposure.

Prompt Injection Enables Agent Goal Hijacking

Attackers can manipulate inputs to alter an autonomous agent's intended goals and execute unauthorized actions. This can lead to agents improperly accessing internal tools or modifying databases without human oversight.

AI Designs Compound Standard Security Framework Risks

Securing AI applications requires addressing vulnerabilities across multiple OWASP Top 10 lists, including API, LLM, and agentic risks. This intersection creates a massive landscape of potential security flaws that teams must continuously monitor.

AI Firewalls Must Replace Basic Guardrails

Traditional guardrails are too limited because they only protect specific prompts and responses at the language model level. Comprehensive protection requires AI firewalls distributed throughout the architecture to secure agents and protocols.