Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Hugging Face Incident Proved the Real AI Risk Is in the Action Layer

Last week, an AI system crossed a line many still considered theoretical. During an internal cybersecurity evaluation, OpenAI tested a combination of models, including GPT-5.6 Sol and a more capable pre-release model, on ExploitGym, a benchmark that measures whether agents can turn software vulnerabilities into working exploits. The models were run with reduced cyber refusals and without the production classifiers normally used to prevent high-risk cyber activity.

AI Data Pipeline Security: How to Protect Personal Data Before, During, and After Model Use

Artificial intelligence is reshaping how enterprises process information, but it is also redefining where sensitive data is exposed. Every prompt, retrieval request, API call, and AI-generated response creates another opportunity for personal or confidential information to move beyond its intended boundaries.

Runtime Security for LLM Applications: How to Monitor Prompts, Context, Tools, and Outputs

Large language models are becoming the operational layer behind enterprise AI, powering intelligent assistants, automated workflows, and AI agents that interact with sensitive business systems. But as LLMs process confidential prompts, retrieve enterprise context, and execute connected actions, every runtime interaction introduces new security risks.

The Open-Source Paradox: Navigating the New Frontier of AI Supply Chain Risk

The recent developments surrounding vulnerabilities in major AI repositories like Hugging Face serve as a critical wake-up call for the cybersecurity community. As we accelerate toward an agentic future, the platforms we rely on for innovation are increasingly becoming the primary vectors for systemic risk.

Introducing The Hybrid Nudge Experience: Outbound Email Security Built for Your Risk Appetite

When it comes to outbound email security, every organization operates under different operational constraints and security requirements. Some security teams prioritize in-app nudges and coaching to catch risky behavior the moment an email is drafted. Others want to avoid friction, particularly for executives, sales teams or mobile-first employees who rarely interact with desktop add-ins.

Why Most Automation Projects Stall Before They Reach ROI

Automation has a credibility problem hiding behind its success stories. For every celebrated deployment, there's a quieter statistic: analysts and consultancies have repeatedly found that a large share of automation initiatives - by some estimates a third to half of early RPA programs - fail to scale or deliver their expected return. The technology usually works; the project is what breaks.

Understanding Context Windows in AI-Powered Security Operations

Your security operations team now relies on AI agents to detect threats, triage alerts, and accelerate incident investigation. These agents analyze signals across your environment to identify suspicious behavior that humans might miss, and they respond faster than any manual process could. But they operate under a fundamental constraint that most security teams overlook: context window limitations that directly impact investigation quality and threat visibility.