Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Everyone Is Buying AI Guardrails. But Agents Have the Keys to the Car.

The first wave of AI security looked a lot like a WAF for LLMs: inspect the prompt, filter the output, block the obvious bad patterns. That was useful. It still is. But it was built for systems that mostly talked. Agents are different. They use tools, call APIs, access data, and change things. The confusion I keep seeing is simple: many teams think securing the model means securing the agent. It does not.

The Meta AI Chatbot Did Exactly What it Was Asked. That Was the Vulnerability. Why Business Logic Security is the Foundation!

An account-takeover campaign against Instagram shows why agentic AI inherits every business logic blind spot we already had and then hands it a megaphone. Over the past weekend, a number of Instagram users, including the long-dormant Obama-era White House handle and a U.S. Space Force senior enlisted leader found their accounts hijacked. As reported by TechCrunch, the entry point wasn’t a stolen password, a phishing kit, or a zero-day in Instagram’s code.

Salt Cloud Connect for Github

Your developers are shipping agents, MCP servers, and APIs faster than security can see them. GitHub Connect changes that. Salt scans your repositories and surfaces every agent, MCP server, and API hiding in your codebase, then maps them into the Agentic Security Graph. You see the agentic infrastructure forming in code, before it ever reaches production. No more waiting for runtime to find out what shipped. No more blind spots between dev and prod. Govern what's being built from day one.

Introducing the Wallarm AI Control Platform: One closed loop for AI security and API security.

Every week, someone in your organization stands up an AI service. Maybe they told security about it, but probably not. By the time it shows up in your inventory, it has been running for weeks, processing data, calling external APIs, and doing things nobody formally reviewed.

MCP vs. Traditional API Security: Why Your Existing Controls Don't Protect MCP-Powered AI Agents

Traditional API security protects deterministic systems with known endpoints and explicit actions, while MCP-powered AI agents operate through inferred intent, dynamic tool chaining, and natural language interactions. This requires MCP-specific security controls such as tool governance, behavioral monitoring, and semantic anomaly detection.

Even Google says you cannot do AI security on one platform

This week, Connie Loizos, editor in chief of TechCrunch, sat down backstage with Francis de Souza, COO of Google Cloud, for a piece on the state of enterprise AI security. The interview is worth reading in full. Three points in it should reshape how every CISO is thinking about the next twelve months.