Risks in AI-Native Systems: Why AI Security Is Still an API Security Problem | On-demand Webinar | Harness Resources
Webinar: On-Demand
Webinar: Upcoming Event
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 systems
Why limited visibility and control across the AI and API ecosystem creates significant security risk
How organizations can apply proven API security practices to AI-driven environments
Strategies for improving AI discovery, testing and protection across AI-native applications.
New Harness research reveals how rising AI costs, ownership gaps, and limited cost visibility are preventing enterprises from maximizing AI ROI and controlling AI spend.
In this session, we’ll explore how this shift is taking shape. We’ll look at the principles behind unifying infrastructure and database workflows, how leading teams are reducing risk while increasing delivery velocity, and what it takes to bring stateful systems into the same continuous delivery model as application code.
In this presentation, we’ll explore how modern experimentation practices can be applied to infrastructure workflows, from feature flags and gradual rollouts to real-time monitoring and automated kill switches.