Category: AI Security
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Data Leakage in AI Systems: Risks, Patterns, and How to Prevent Them
Data leakage is one of the most serious risks in AI deployments, and it is also one of the least visible. Unlike a traditional data breach — which typically involves an attacker gaining unauthorized access to a database or system — data leakage in AI systems often occurs through the AI’s normal operation, without any…
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Enterprise AI Governance: How to Build an MCP Tool Audit Program
Enterprise adoption of AI agents has accelerated dramatically over the past two years, but governance frameworks have not kept pace. Many organizations have deployed AI systems that connect to dozens of external tools and data sources without establishing clear policies for how those connections are selected, monitored, or terminated. As regulatory scrutiny of AI systems…
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Prompt Injection in AI Agents: The Developer’s Guide to Detection and Defense
Prompt injection is rapidly becoming one of the most consequential security vulnerabilities in AI systems. Unlike traditional software vulnerabilities that exploit bugs in code, prompt injection exploits the AI model’s core design: its ability to follow natural language instructions. When an AI agent processes content from an external source — a web page, a document,…
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MCP Security Best Practices: Protecting Your AI Pipeline in 2026
As AI agents become increasingly capable of taking real-world actions — sending emails, querying databases, executing code, and interacting with third-party APIs — the security of the tools they connect to has become a critical engineering concern. The Model Context Protocol (MCP) has emerged as the dominant standard for connecting AI systems to external capabilities,…