Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Colorado passed the first comprehensive state AI law in May 2024, and organizations spent the following year building impact assessment processes against it. Those obligations never took effect. The statute was delayed twice, blocked by a federal court, then repealed and replaced by a narrower framework before its own effective date arrived. ‍ Anyone who built a compliance program to that specific statute prepared for a regime that never existed.

As artificial intelligence becomes deeply woven into daily workflows, employees are adopting unapproved AI apps at an unprecedented pace. According to Teramind’s Shadow AI Behavior Report, a staggering 89% of workplace AI usage occurs outside enterprise-governed channels, leaving 86% of organizations blind as to how corporate data flows in and out of these tools.

Jailbreak prompts (i.e. prompts designed to remove or bypass the guardrails and rules that govern AI systems, like LLMs) prompts have been circulating for years. At first, a lot of it was pretty simple: copy a prompt, tell the model to ignore its rules, and see what happens. It was also largely noisy, unverified, and often didn’t work. But the noise was still telling us something. Threat actors were beginning to study AI systems the same way defenders were, and over time, the goal started to change.

Most organizations spent years hardening their software supply chain. The model is familiar: dependencies flow through a controlled repository, policies determine what is allowed, scanning catches what slips through, and every action is logged for auditability. It works because human developers operate within environments that enforce these rules. AI coding agents break that assumption entirely.

Protecting sensitive data is no longer just a compliance objective. It has become a prerequisite for adopting cloud services, enabling AI, and maintaining customer trust. Yet many organizations still struggle to balance innovation with effective data governance. A 2025 Forrester Total Economic Impact study commissioned by Microsoft found that organizations using mature data protection capabilities, including data loss prevention (DLP), achieved a 30% reduction in the likelihood of data breaches.

Along with the Bank of England in May, the ECB is requiring organisations to produce an action plan against AI-driven cyberattacks (opens in a new tab). What it wants, however, is not a strategy paper but proof of operational readiness.

Healthcare has always run on two currencies: information and trust. Patients hand over their most sensitive data, diagnoses, genetic profiles, mental health histories, on the assumption that it will be protected and that the people (or systems) handling it will treat them with care. Artificial intelligence is now reshaping both sides of that bargain at once.