Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

We’ve all seen it: a cutting-edge, multimodal LLM, capable of understanding complex documents, stumbles on a seemingly simple task. In our case, the model confidently reported a contract’s signing date as "March 30". The only problem? The document clearly stated "March 9th". It wasn't just a minor error; it was a baffling one that sent us down a rabbit hole of debugging.

You got compliant—congrats! That’s a big milestone. It tells customers, investors, and the world that you take security seriously. But compliance doesn’t stop at your first audit. As your company grows, so do the requirements. You’ll have to manage new frameworks, more policies, faster timelines, more scrutiny, and more complexity. ‍ Modern GRC teams need to do more with less.

If your organization has a Microsoft Azure Consumption Commitment (MACC), you’re already on the path to optimizing cloud spend. But are you making the most of it? MACC isn’t just a budgeting tool, it’s a strategic advantage. By transacting eligible solutions through the Azure Marketplace, you can decrement your commitment while accelerating your cloud security and operations. That’s where BlueVoyant comes in.

What’s the fastest way to lose trust? Expose private data. With AI moving from pilots to core workflows in support, finance, HR, and healthcare, one careless prompt or leaky integration can turn into headlines, fines, and weeks of incident response. The most useful way to understand the risks is to study AI privacy issues examples from the real world.

In this series, we examined the vital connection between AI and APIs, highlighting what makes a leader in the API security market through the 2025 KuppingerCole Leadership Compass. Now, we turn to the core strategy of true API security: the full-lifecycle approach, where security is a continuous, integrated process rather than a single action.

The Cloud Security Alliance, a respected non-profit founded in 2008 to pursue cloud security assurance, has now unveiled its Artificial Intelligence Controls Matrix (AICM), a quiet revolution for trustworthy AI. It has come at a time when generative AI and large language models are moving quickly into every sector. These systems can transform business, but they can also fail, or be made to fail. Because of this, trust becomes the measure of success.

In Part 1 of this blog series, we explored the architecture, capabilities, and risks of the Model Context Protocol (MCP). In this post, we will focus on two attack vectors in the MCP ecosystem: prompt injection via tool definitions and cross-server tool shadowing. Both exploit how LLMs trust and internalize tool metadata and responses, allowing attackers to embed hidden instructions or persistently influence future tool calls without direct user prompts.