The article explains how AI has drastically reduced the time attackers need to weaponize vulnerabilities—from an average of 771 days in 2018 to approximately 4 hours in 2026. This rapid exploitation pace makes traditional patching cycles impractical. Enterprises must therefore adopt a multi-layered approach to application security that includes accurate inventory tracking, continuous risk assessment, and vulnerability scanning. It advocates streamlining patching processes and leveraging threat intelligence to anticipate emerging threats. Preventive and runtime security controls, including protections at the application, API, and AI layers, are critical to compensate for the inability to patch immediately. The article also discusses the security implications of agentic AI, recommending controls such as DDoS and bot protection, malicious user detection, and continuous monitoring of agent activities. Overall, it calls for a strategic shift in application security to address the accelerated threat landscape driven by AI.

The accelerated timeline from vulnerability disclosure to exploitation significantly increases the risk that enterprises will be compromised before patches can be applied. This heightens exposure to attacks targeting unpatched applications, APIs, and AI components. The inability to keep pace with patching cycles may lead to increased exploitation opportunities. Additionally, the rise of agentic AI introduces new attack vectors that require enhanced detection and mitigation capabilities. Enterprises face increased operational challenges in managing application security risk under these conditions.

The article recommends enterprises implement accurate and comprehensive inventory management of applications, APIs, and AI components to ensure visibility. Continuous risk assessment and vulnerability scanning should replace traditional periodic assessments to keep pace with rapid threat evolution. Patching processes must be streamlined to enable more frequent updates. Enterprises should invest in mature threat intelligence programs to anticipate emerging threats. Preventive controls should be tightened, and runtime security measures deployed across all application layers, including AI-specific protections. To address risks from agentic AI, enterprises should deploy application-layer DDoS protection, bot protection, malicious user detection, and continuous monitoring of agent behavior. These combined measures help mitigate risk despite the impracticality of immediate patching.