Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every certificate renewal automation tool has to answer one architecture question before it does anything else: where does the private key get generated? A reader who used to be “the certificate guy” at his organization emailed me this week to ask about exactly that: It’s the right instinct. It’s also how most automation tools work. Certbot generates the key on the server, builds a certificate signing request, and the private key never leaves the machine.

From Chaos to Compliance: Mastering Your SOC 2 Audit Preparing for a SOC 2 audit usually starts the same way. A customer asks for your report, sales says the deal is blocked without it, engineering already has half the controls in place, and nobody can prove any of it cleanly. The problem usually isn't a total lack of security. It's fragmented evidence, inconsistent ownership, and controls that exist in practice but not in auditor-ready form.

It was time to write another book. That’s what I thought when I heard that Corelight wanted to update its 2021 book on network detection and response (NDR). Tamara Crawford, who owned the project, scheduled a meeting with me and asked if I might be interested in helping, depending on who might write the text.

IBM’s 2025 Cost of a Data Breach Report found that 97% of organizations that experienced an artificial intelligence (AI)-related security incident lacked proper access controls on AI systems. The same report highlighted that 63% of organizations lacked governance policies to manage AI or prevent shadow AI. Despite those statistics, AI is now deeply embedded in workflows across critical business functions. Employees are using public AI tools to work faster.

SCA tools are good at identifying vulnerabilities in your dependencies. They’re not built for the harder part of vulnerability risk management: telling you whether those vulnerabilities are actually reachable in your application, or which assets are running an affected component the moment a zero-day drops. Seemplicity’s SCA Analyst solves both problems inside a single centralized vulnerability management platform.

Static application security testing (SAST) tools are intentionally conservative. Traditional scanners identify code that appears exploitable and flag the snippet for review, even when protections elsewhere in the application prevent exploitation. Although that approach helps teams catch vulnerabilities, it also creates false positives that consume developer time, slow remediation efforts, and make future alerts easier to dismiss.

AI’s latest wave is reshaping cybersecurity in a fundamental way. Capabilities that once were limited to a handful of frontier models are now widely accessible, cheaper, and embedded across more environments. As access expands, risk is growing fast and scaling even faster.

For years, development and security practitioners have treated dynamic application security testing as a critical final safety check before code goes live. However, the external attack surface is expanding faster than mid-sized security teams can realistically manage. In this high-velocity environment, legacy DAST tools are increasingly failing to keep pace.

Right now, a bot is running a single command against a website and reading the first few lines that come back. Maybe yours. It is not personal. The bot is working down a list of a few hundred thousand WordPress sites, and any given site is on it because WordPress runs more than 40% of the web and the same small set of mistakes shows up on most of them. You can read exactly what that bot reads. It takes about ten minutes, the tools are already on your machine, and none of it is hacking.