Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI models rarely become a governance problem because of the model alone. The real risk often emerges from what surrounds it: the data it receives, the decisions it influences, the systems it can access, and the people accountable for its outcomes. That makes AI model governance a lifecycle discipline, not simply a model approval process. Even NIST’s AI Risk Management Framework treats governance as a function that cuts across the entire AI lifecycle.

Prompt injection has not changed since 2022. What the model can do has. The instruction that hijacked a translation bot four years ago and the one that opened a homeowner’s windows last year are the same request: do something you are already allowed to do. The first system could only talk. The second could operate devices. Same sentence, different consequence. Here are ten real attacks, in order, and for each one the thing the system was allowed to do that made it work.

Prompt injection is three things happening at once. The definition written in 2022 described a model that followed an instruction hidden in the text it was asked to translate. The definition needed in 2026 describes an agent that read a support ticket and then queried a customer table it had never touched, using a service account nobody had revoked. Those are the same attack.

Connecting ChatGPT, Claude, or another AI agent to WordPress can turn simple instructions into real website actions. An agent can create posts, update pages, upload media, manage orders, and handle other tasks when the right MCP tools are available. The interesting part starts when you decide how much access to give it. A content management agent may need posts and media, while a WooCommerce support agent may need orders and customer information.

Security teams face a flood of noisy logs that arrive in many formats, and often without the context teams need to detect and prioritize threats. But even normalized and contextually enriched log data is only as useful as the speed at which teams can act on it. Any step that requires manual effort can become a bottleneck that makes it difficult to keep pace with the volume.

For an individual, self-hosting a password manager is rarely realistic. Most people don't run servers, handle patching, or manage uptime, so a cloud vault ends up being the only practical choice. For an organization, self-hosting becomes a real decision, one your security and IT teams can make deliberately. And once you can choose, you also take on the risks and the responsibility that come with that choice.

Modern bot threats are increasingly driven by determined, sophisticated attackers. Often it is not even one person, but a group trading techniques with each other or a commercial service sold to anyone willing to pay. For many of them, getting past bot detection is a full-time job they genuinely enjoy. Block them and they get to work, finding a workaround. AI has simplified this further, making it even easier to set up complex configurations for attackers, lowering the overhead of an attack.

Artificial intelligence is no longer a future consideration for MSPs because it is already reshaping how leading providers run their businesses, protect their clients, and create new revenue streams. Separating real business value from hype requires a clear-eyed, practitioner-driven perspective. Join Marc Laliberte and a panel of MSP and security operations leaders for a candid discussion on how AI is being deployed today.

Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Every AI security framework names the risks you have to control. Zenity is built to implement those controls at runtime. Here's the 2026 OWASP Top 10 for LLM Applications, entry by entry, with the gaps marked honestly. Paste a booby-trapped instruction into a chat window, and nothing much happens.

CVE-2026-12569, PTC Windchill, and the implant that never touched an EDR.