Artificial Intelligence (AI) has become the definitive frontier of digital defense, acting as both a sophisticated shield for defenders and a high-speed engine for adversaries. In a landscape where the volume of data exceeds human processing capacity, AI is essential for cybersecurity because it enables real-time anomaly detection, predictive threat intelligence, and autonomous incident response at machine scale. However, this same technology introduces unique vulnerabilities—such as prompt injection, data poisoning, and the rise of hyper-realistic deepfakes—that require fundamentally new security architectures. By integrating AI-driven defenses while maintaining rigorous governance over the models themselves, organizations can transform their security posture from reactive firefighting to a proactive, resilient strategy that evolves as quickly as the threats it faces.
Released in February 2026 by the OWASP GenAI Security Project, the Practical Guide for Secure MCP Server Development provides a comprehensive framework for securing the connection between AI agents and… Read More »OWASP Practical Guide for Secure MCP Server Development
The NIST Cyber AI Profile offers a structured approach to integrating AI into cybersecurity operations. It provides guidance on leveraging AI for threat detection, risk assessment, and automated response while… Read More »NIST IR 8596 Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile): NIST Community Profile ★★★★★
ETSI EN 304 223 is a landmark European standard, published in early 2026, that establishes the first globally applicable baseline for securing artificial intelligence models and systems throughout their entire… Read More »ETSI EN 304 223: Securing Artificial Intelligence (SAI) – Baseline Cyber Security Requirements for AI Models and Systems
The Cloud Security Alliance (CSA) provides comprehensive guidelines on data security within AI environments, emphasizing a lifecycle approach to protect sensitive training data, models, and inference inputs from threats like… Read More »CSA Data Security within AI Environments
The CISA Principles for the Secure Integration of Artificial Intelligence in Operational Technology provide a comprehensive framework to help critical infrastructure owners and operators securely integrate AI into their OT… Read More »CISA Principles for the Secure Integration of Artificial Intelligence in Operational Technology
The OWASP AI Testing Guide provides a comprehensive framework for evaluating the security and reliability of AI systems. It outlines methodologies to identify risks such as model poisoning, adversarial attacks,… Read More »OWASP AI Testing Guide ★★★★★
The CSA AI Controls Matrix (AICM) is a comprehensive framework designed to help organizations assess and manage the risks associated with artificial intelligence (AI) systems. It outlines a set of… Read More »CSA AI Controls Matrix (AICM) ★★★★★
The CSA’s paper “Agentic AI Identity and Access Management: A New Approach” addresses the inadequacy of traditional IAM systems when applied to autonomous, dynamic agentic AI systems operating in multi-agent… Read More »CSA Agentic AI Identity and Access Management
The OWASP AI Maturity Assessment (AIMA) provides a structured framework designed to help organizations evaluate and improve the security, trustworthiness, and compliance of their AI systems. It is organized into… Read More »OWASP AI Maturity Assessment (AIMA)
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a comprehensive, publicly available knowledge base and framework that documents the tactics, techniques, and case studies of adversarial attacks targeting artificial… Read More »MITRE ATLAS version 4.8.0