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Reporting on how AI changes cyber defense and abuse, from model security and automated SOC workflows to deepfakes, prompt attacks, and AI-enabled threat activity.
Based on the CRN CEO Outlook 2026, analyze global security vendor CEOs' assessments of AI's impact on both attack and defense, and provide recommendations for enterprise responses.
In-depth analysis of the arms race between AI attacks and AI defense, exploring how generative AI lowers the barrier to attacks, how autonomous AI transforms security operations centers, and enterprise response strategies.
Generative AI code generation improves efficiency while introducing risks such as injection attacks and unsafe code templates. A new study proposes a hybrid ANN-ISM framework that combines predictive analysis with structured risk management, helping enterprises systematically mitigate these security challenges.
Based on the 2026 AI Security Report released by Check Point Research, analyze how AI has evolved from an attack auxiliary tool to a real-time attack operator, and its impact on enterprise security.
Based on research from Michigan Technological University, this analysis examines the impact of AI on computing professions, particularly in the field of cybersecurity, providing talent strategy references for enterprise security decision-makers.
Check Point Global CTO Jonathan Zanger pointed out at the Engage 2026 conference that AI is profoundly transforming cybersecurity from three dimensions: scaling defenses, expanding attack surfaces, and the need for explainability. Enterprises must embed security layers from the very start of AI projects to address the new risks posed by non-deterministic systems.
Cybersecurity risk assessment is a core responsibility of the CISO, but many organizations fall into common pitfalls during implementation, such as formalization, scope omissions, and confusing compliance with security. This article analyzes seven major misconceptions and their actual impact on enterprise security, and provides professional recommendations for addressing them.
Security vendors have discovered that the macOS malware Gaslight uses prompt injection techniques to command LLM-assisted analysis tools to stop detection. This trend indicates that AI security defenses are facing new adversarial methods, and enterprises need to be wary of the vulnerability of relying on a single AI detection system.
Security Operations Centers (SOCs) have long faced a triangular trade-off between quality, consistency, and cost efficiency. AI is changing this structural constraint, enabling enterprises to simultaneously improve all three for the first time, thereby reshaping the economics of security operations. This article, based on insights from industry experts, provides an in-depth analysis of AI's impact on SOC workflows and the strategies enterprises can adopt.
Generative AI, agentic AI, shadow AI, machine learning, and general artificial intelligence are reshaping the cybersecurity landscape. SecurityPost synthesizes insights from dozens of experts to provide a comprehensive assessment of the current state of AI and cybersecurity for enterprise security decision-makers.
Researchers at the University of Toronto demonstrated a prototype of an AI worm based on an open-source LLM, which autonomously replicates in a simulated network and exploits known vulnerabilities and common configuration flaws, indicating that the threat of new automated attacks facing enterprises is increasingly imminent.
Based on SecurityWeek’s report and Adversa AI’s AI Risk Quadrant analysis, this article interprets the security assessment results of 100 AI agents, focusing on the implications for enterprises of the “capability-defense inversion” and the triad of fatal combinations, as well as how CISOs should respond at the identity, outbound control, supply chain, and governance levels.