Genai Security
Genai Security is a specialized security framework designed to address vulnerabilities and risks specific to AI agentic applications. These systems utilize language models and other AI components that possess autonomous decision-making capabilities, allowing them to take independent actions in external environments. Unlike traditional application security frameworks that primarily focus on protecting static software interfaces and preventing unauthorized access to code or data, this framework accounts for the dynamic and unpredictable nature of AI-driven workflows.
The framework explicitly maps to the OWASP Top 10 risks for Large Language Model applications, adapting standard security controls to the unique threat landscape of generative AI. It addresses issues such as prompt injection, where malicious inputs manipulate model behavior, and data exfiltration, where sensitive information is inadvertently revealed through model outputs. By focusing on the interaction between the AI agent and its environment, the framework provides guidelines for securing the decision-making loop rather than just the underlying infrastructure.
Implementation of Genai Security requires a shift from perimeter-based defenses to continuous monitoring of model behavior and output integrity. Security teams must evaluate the trustworthiness of the data sources feeding the agents and the safety of the actions the agents are permitted to execute. This approach ensures that autonomous systems operate within defined boundaries, mitigating risks associated with hallucination, bias, and unauthorized API calls while maintaining the utility of the agentic workflow.
Source Notes
- 2026-04-08: Top 10 Security Risks in AI Agents Explained
- 2026-04-07: OWASP Top 10 Security Risks for AI Agentic Applications Report · ▶ source
- 2026-04-21: Claude Mythos · ▶ source