Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The history of computing is marked by sea change moments; those times when the world seems to shift into a new possibility space almost overnight. ENIAC. The personal computer. The World Wide Web. The smartphone. And now, AI. While the term “AI” has been applied to many new (or re-branded) services and products, the underlying technology that makes most of them feel like magic is the large language model (LLM).

Network Security Policy Management (NSPM) is the discipline of defining, enforcing, and maintaining the network policies that govern how systems communicate, what data moves where, and who can access what. It sits at the intersection of security and operations, helping organizations maintain consistent, enforceable rules across increasingly complex infrastructures.

You’ve probably seen a file like this sitting in your project root.

Earlier this month, Microsoft uncovered SesameOp, a new backdoor malware that abuses the OpenAI Assistants API as a covert command-and-control (C2) channel. The discovery has drawn significant attention within the cybersecurity community. Security teams can no longer focus solely on endpoint malware. Attackers are weaponizing public and legitimate AI assistant APIs and defenders must adjust.

Your manufacturing environment might be compliant, but that doesn't mean it's protected against a cyberattack. Even compliant systems can fall victim to cybercrime. And the consequences can be devastating. The SANS Institute sends a clear message in its 2025 State of ICS/OT Cybersecurity Survey: Regulatory compliance alone is no longer enough in operational technology (OT) environments.

Can you ever imagine the impact on your business if it went offline on Black Friday or Cyber Monday due to a cyberattack? Black Friday is the biggest day in the retail calendar. It’s also the riskiest. As you gear up for huge surges in online traffic, ask yourself: have you protected the APIs on which the business runs?

SAST (Static Application Security Testing) tools analyze an application’s source code to identify potential security vulnerabilities without executing the code. They are crucial for finding security flaws early in the development lifecycle, helping developers address issues before they become more costly and difficult to fix. Unlike dynamic analysis techniques, SAST operates without executing the program, focusing entirely on the static codebase.

The UK government introduced the Cyber Security and Resilience Bill to Parliament on November 12th, 2025. Science, Innovation and Technology Secretary Liz Kendall stated: “Cybersecurity is national security. This legislation will enable us to confront those who would disrupt our way of life.” If you work in healthcare, energy, water, transport, or supply IT services to these sectors, this legislation will directly affect how you manage cybersecurity.

Generative AI is rapidly transforming the way we work. The large language models (LLMs) that power tools like ChatGPT and Claude are immensely powerful, capable of providing us with research data, detailed insights, and even deep analysis of documents and data sets, all performed through simple, text-based prompts. However, these prompts have unfortunate side effects for the IT professionals assigned to protect sensitive and proprietary data from cyber attacks.

Website data leaks don’t require hackers. They happen when legitimate scripts, analytics pixels, and chat widgets transmit sensitive data to third parties through routine operations. Traditional security tools miss these leaks because they monitor server-side traffic while the exposure occurs in customer browsers. This visibility gap is why organizations use client-side monitoring platforms to detect browser-level data flows that security tools can’t see.