Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The AI governance confidence gap: Why trust in AI is running ahead of the capacity to govern it

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

BGP ORIGIN attribute manipulation and its impact on the Internet

Border Gateway Protocol (BGP) is the de facto routing protocol of the Internet. It offers built-in mechanisms to allow entities, represented by Autonomous Systems (ASes), to express how they want to send and receive traffic on the Internet. One such mechanism is path attributes, which carry essential routing information and metadata for their associated route.

AI-Generated Phishing Achieves a 54% Click Rate

For years, phishing has worked for one simple reason: it exploits the weakest link, the user. The defensive strategy has followed the same formula: better email filtering, more user awareness, and an extra layer of authentication. It wasn't perfect, but it was a workable balance.

Best Cloud Penetration Testing Providers in 2026

Most cloud breaches begin with a configuration error the customer made. Gartner projected that through 2025, 99% of cloud security failures would be the customer’s responsibility, caused by misconfigured identity and access management, exposed storage, and over-permissioned services. Cloud penetration testing is the simulation of real-world attacks against cloud infrastructure on AWS, Azure, and GCP to find those exploitable gaps before an attacker does.

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.

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.