Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Improve API authentication detection with Datadog

Many organizations have hundreds or thousands of API endpoints across their services, each of which handles authentication differently. For example, one service might rely on standard headers like Authorization: Bearer, while another uses an API key, and a third uses a custom JSON Web Token header with mechanisms or naming conventions specific to the team that built it.

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.

New Security Gap: Your WAF Has No Idea What Your AI Is Doing

In this webcast, we get into why signature-based protection breaks down in AI-first environments, what behavioral detection and positive security models actually look like in production, and what it takes to evaluate whether your runtime tools are genuinely adapting to your environment or just adding noise to your stack.

Next.js Vulnerability Exposes Credentials and Protected Data - Why Runtime API Security Matters

A newly disclosed security issue, tracked as CVE-2026-44578, affecting Next.js applications is raising concerns across the developer and security communities after researchers identified multiple authorization bypass and middleware evasion paths that could expose protected application data and credentials. The vulnerabilities impact several versions of Next.js and allow attackers to bypass middleware-based authorization controls using crafted requests and route manipulation techniques.

The Agentic Security Graph: Get Visibility into your AI Security Risks

As enterprises shift from conversational to agentic AI, the real risk moves from model outputs to the action layer; the MCP servers and APIs through which agents execute real-world tasks. The Agentic Security Graph frames this risk across three interconnected layers (LLM, MCP servers, APIs), showing how compromises at any layer can propagate and why existing LLM-focused controls leave the most consequential surface unmonitored.

What Your Board Gets Wrong About AI Security

Editor's note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author's permission. Boards are giving AI security more airtime than ever. What they're not giving is the right framing. A year or two ago, AI was mostly a question of experimentation risk. Today, it's tied directly to revenue, customer experience, operational efficiency, and competitive advantage. The urgency is real, and it's translating into aggressive deployment timelines.

Salt Agentic Security Platform

Most enterprise AI security investment is focused on the model layer—guardrails, output filtering, LLM governance. That's necessary. It's not sufficient. AI agents take actions: they call APIs, invoke MCP servers, access databases, and trigger downstream workflows. The Salt Security Agentic Security Platform was built to secure that action layer (the infrastructure your agents actually operate across).

Your AI Agents Are Already Acting. The Question Is Whether You Can See What They're Doing.

In conversations with CISOs about their agentic environments, the question I ask first is not whether they have agents deployed. Most do. It is not whether those agents are creating value. Most are. The question I ask is whether they have mapped their Agentic Security Graph. Almost none of them have. And that gap, between the agentic infrastructure that exists inside their organizations and the visibility they have into it, is where the most serious AI security risk in the enterprise lives right now.