Houston is one of America’s most commercially active cities — a Fortune 500 corridor, a booming technology sector, and tens of thousands of small and mid-size businesses processing credit and debit card payments around the clock. Every one of those businesses is legally bound by a set of security standards that most owners know surprisingly little about: the Payment Card Industry Data Security Standard, universally referred to as PCI DSS.

As more businesses switch to online operations, it becomes increasingly important to have safe, secure websites. Cyber attackers are targeting websites to steal sensitive data, demand ransom payments, and disrupt business operations. To prevent this, organizations must invest in website penetration testing. Penetration testing, also called pentesting, is a process of simulating cyberattacks to identify security gaps in a website.

By rewriting our secret detection engine in Rust, we made our engine more than three times as fast. But not without making it four times slower along the way.

Traditional API security protects deterministic systems with known endpoints and explicit actions, while MCP-powered AI agents operate through inferred intent, dynamic tool chaining, and natural language interactions. This requires MCP-specific security controls such as tool governance, behavioral monitoring, and semantic anomaly detection.

Automated vulnerability remediation uses policy-driven workflows to execute approved remediation actions, including patch deployment, software updates, and configuration changes, consistently across managed assets. Within a broader vulnerability management program, it helps teams close the gap between identifying an exposure and safely resolving it at scale.

Earlier this year, BlueVoyant adopted a new detection strategy built on the Advanced Security Information Model (ASIM). For those unfamiliar, ASIM is Microsoft's normalisation layer that standardises log data across products into consistent schemas. Our approach is simple: The result? Dramatically faster use case development and cleaner, more maintainable detection logic.