Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

From inchoate brainstorming sessions in the halls of Dartmouth College to a panoply of funding springs and winters, AI has made its way into the tech stack of not just almost every enterprise but also every household. This, though music to the ears of an AI researcher, rewards a security professional with sweat beads. Even a single AI/ML/LLM or an MCP feature in your product evolves your attack surface, necessitating scouting for the right AI/ML/LLM/MCP penetration testing companies.

So, you have some AI tools or are thinking about deploying them and want to know a bit about securing them. You are not alone, but there are significant challenges due to the rapidly growing capabilities of AI, and the issues around new types of vulnerabilities we may not be used to thinking of. This is a very challenging area to attempt to secure, but I hope to point you in the right direction and set you up with some resources.

Healthcare has moved well past pilot projects. AI agents now triage support tickets, draft clinical documentation, manage patient engagement, and coordinate care across systems that were never designed to talk to autonomous software. Autonomous systems can now analyze data, make decisions, trigger actions, and coordinate across clinical systems with minimal human oversight.

Most CTI programs aren’t failing because analysts lack skill. They’re failing because signal volumes have outpaced what any manual workflow can process. Thousands of newly registered domains, phishing kit variants, and brand impersonation attempts surface daily. Human teams can’t triage all of it. Threat intelligence automation addresses the throughput problem by automating collection, enrichment and prioritization so analysts spend time on decisions, not data wrangling.

The first time an MCP (Model Context Protocol) server felt real to me, it wasn't because of a clean demo. It was because of the noise. TL;DR: The harness matters more than the protocol, and the evidence matters more than both. MCP earns its keep when it shortens the path from a good security question to trustworthy evidence, and almost everything interesting about making that work happens in the harness wrapped around the model. In this series, I will cover how to build an MCP for an AI SOC.

AI agents introduce a category of security risk that traditional application security, identity management, and even standard AI security programs were not designed to handle. Unlike a generative model that only produces text, an agent takes autonomous action against real systems, chains API calls together to accomplish goals, and often holds permissions broad enough to touch data across multiple business systems.

Artificial intelligence is quickly becoming one of the most transformative technologies in cybersecurity. Unfortunately, it's not transforming the industry exclusively for defenders. Attackers are already experimenting with AI to accelerate reconnaissance, analyze software for vulnerabilities, develop fully-functional exploits, and automate nearly all parts of the attack lifecycle.