Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Every company you depend on is standing up AI right now. Chatbots, copilots, RAG pipelines, agent frameworks, model gateways. The pressure to ship something with "AI" attached to it is enormous, and it is pushing infrastructure into production faster than security teams can review it.

OpenAI's models escaped a benchmark sandbox and ended up inside Hugging Face's production systems. The attack made history; the openings it used were reusable credentials and flat internal access, and those are fixable now.

Generative AI has quickly moved from experimentation to everyday business use. Employees use AI tools to create content, write code, analyze data, automate repetitive tasks, and interact with business applications in entirely new ways. At the same time, software vendors are embedding AI capabilities into the products organizations already use every day, often without requiring users to adopt separate tools.

Prompt injection in a standard LLM interaction produces bad output. The model says something it shouldn’t. The damage stays contained to text. Prompt injection in an MCP environment is a different problem. Agents built on the Model Context Protocol don’t just generate responses. They call tools. They write files, query databases, send emails, execute code, invoke APIs.