Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You deployed AWS WAF, completed the initial setup, and got visibility into your traffic. Then the operational reality sets in. Teams that find their way to this comparison typically share one of three experiences: If any of those match where you are, this guide will tell you what you need to know.

The number of publicly reported unique vulnerabilities has risen year after year. There was a brief decrease and stabilization in 2015 - 2016, but those are the only years in the over two decades (1999 - on) I have been following vulnerability metrics. Other than that, it has been up, up, up.

I need to confess something. A few days ago whilst vibe coding at 2am (which can end up burning through tokens like they are going out of fashion) I accidentally pasted my API key directly into a Claude chat instead of the terminal window I had open. Claude told me off. It felt like a full, proper, disappointed parent tone; the AI equivalent of 'I'm not angry, just disappointed', except it absolutely was angry. There may have been paragraphs.

On March 9th, Codewall.ai disclosed how it had hacked McKinsey & Company’s AI platform called Lilli, a purpose-built system for 43,000+ employees to analyze documents, chat, and access decades of proprietary research. The researchers unleashed an AI agent which quickly scanned 200 endpoints, identified 22 that did not require authentication, and one that wrote user search queries into a database including non-parameterized JSON keys which were concatenated directly into SQL.

Every major shift in security operations starts with a shift in the underlying platform. The AI era is no different. As artificial intelligence moves from novelty to necessity, the real dividing line in cybersecurity will not be which vendor can add AI features the fastest. It will be which platforms are built on the right foundation to make AI useful in real operations and trustworthy when the stakes are high. That foundation is data, but not in the simplistic sense the market often uses the term.

Over the last year, AI-assisted malware development has evolved from an experimental practice into a common part of the attacker toolkit. In a rolling window from February 2025 to February 2026, Arctic Wolf Labs observed over 22,000 distinct files triggering AI-focused YARA rules across multiple malware repositories. These files included AI-generated code, large language model (LLM)-style scaffolding, runtime AI API integration, and DeepSeek-derived artifacts.

Most organizations share a common, uncomfortable secret: they can’t answer basic questions about what is actually exposed on their IP ranges. As companies grow, whether through decades of history, global data centers, or regional allocations, they lose visibility of their IP footprint. Traditional manual reconnaissance is a point-in-time sync, often leaving security teams blind to what’s actually running on their infrastructure.

Enterprise Agentic AI adoption faces a critical barrier: sensitive data exposure. AI agents perform tasks only as well as the context provided to them. However, context is precisely where enterprise data enters the workflow, introducing significant risk. Organizations need to deploy AI applications while maintaining strict data security, regulatory compliance, and privacy. This challenge stalls production deployments across enterprises, especially in healthcare and financial services.