Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

84% of developers are using or planning to use AI tools in their workflow (Stack Overflow, 2025). AI coding assistants like Codex, GitHub Copilot, and CodeWhisperer are changing how we build software. But here’s the real question: Who secures AI-generated code? In this video, we break down: If you’re using AI to write code, you need: AI-generated code is still code. It must be reviewed, validated, and monitored.

SecurityScorecard's STRIKE Threat Intelligence team has uncovered tens of thousands of exposed OpenClaw instances, many of which are vulnerable to Remote Code Execution (RCE). These exposed OpenClaw instances leave users and organizations open to attacks. OpenClaw and other agentic AI tools are designed to take actions on a user’s behalf, interact with infrastructure, and move across connected services. That functionality is the appeal. It is also the risk for users around the globe.

Managing the risks associated with the increasing use of AI agents and co-pilots is critical for every organization. A key challenge is that AI agents draft documents and influence decisions but they operate without a true understanding of a company's rules, culture, or risk. Like humans, AI agents are susceptible to failure. Humans are socially engineered, while AI agents are prompt engineered, and AI agents may "hallucinate" when context is missing, similar to how humans guess.

See how LimCharlie's Agentic SecOps Workspace handles multi-tenant security operations at scale. This demo walks through real-world scenarios that MSSPs face daily: Unlike token-based AI tools that become cost-prohibitive at scale, this platform uses a flat-fee per analyst model. Every capability in your tech stack becomes accessible through AI-powered automation, and you maintain full control with granular permissions.