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Gartner® Innovation Insight: AI Application Security
AI-enabled applications and agents introduce new risks that traditional application security tools weren't built to address. Gartner explains why AI application security is emerging — and what capabilities organizations should evaluate.
By 2028, at least 50% of organizations that operate public-facing AI-enabled applications will use AI application security capabilities to protect them.
What You'll Learn
In this report, Gartner explores:
Why generative AI applications introduce new risks such as prompt injection and excessive agency
How AI application security combines security testing and runtime defense
The importance of discovery and inventory in identifying rogue or unauthorized AI applications
Why evaluating AI security tools is challenging due to nondeterministic behavior and noise
Key criteria for assessing runtime detection accuracy and remediation capabilities
Why This Matters Now
AI-enabled applications are moving quickly from pilots to production — often customer-facing and deeply integrated with enterprise systems. As organizations scale AI use cases, security leaders need specialized controls that go beyond traditional AppSec approaches.
AI application security tools are emerging to address these risks across the lifecycle — from posture management and adversarial testing to runtime guardrails and anomaly detection.
Protect every AI agent you build and run.
AI security must operate across development and production. A modern AI defense model includes continuous testing, centralized discovery, and runtime enforcement — enabling security teams and engineers to innovate safely without slowing down deployment.