Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Claiming an AI model is too dangerous for public release while issuing a press release about it creates more questions than trust. If something genuinely carries that level of risk, private handling under strict controls makes sense, but public hype only fuels suspicion, competition and panic.

Calling a model too dangerous to release ignores the obvious reality that open and alternative models will soon reach similar capability. Once the path is visible, other providers, including overseas competitors, will build their own versions, so secrecy becomes a temporary market move, not a lasting safety strategy.

Join us for this week's Defender Fridays as Katherine McNamara, Cybersecurity Technical Solutions Architect at Cisco, breaks down the expanding attack surface of AI and ML systems and what organizations need to do to secure them before it's too late. At Defender Fridays, we delve into the dynamic world of information security, exploring its defensive side with seasoned professionals from across the industry. Our aim is simple yet ambitious: to foster a collaborative space where ideas flow freely, experiences are shared, and knowledge expands.

April’s This Month in Datadog spotlights the Datadog MCP Server, which gives AI agents secure, real-time access to Datadog telemetry, and Datadog Experiments, which lets you design, launch, and analyze experiments to see the full impact of product changes on the user journey. Plus, we cover how to: Accelerate Cloud SIEM investigations with Bits AI Security Analyst Remediate vulnerabilities in your codebase with Bits AI Dev Agent for Code Security Explore Datadog with natural language using Bits Assistant.

The volume of AI security research is now too high for any human to track properly by hand. The practical answer is using AI to filter AI, reducing hundreds of articles and reports into a daily shortlist so analysts spend their time on signal instead of noise.