Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Across the Webex link is the 3PAO team sent to pass judgment on your adherence with FedRAMP 20x. Like all auditors, they're diligent, professional, and particularly unnerving when they’re trying to be disarming. It’s the first interview where you explain what the product does.

One recurring theme in my research and writing on agentic AI security has been the distinction between soft guardrails and hard boundaries. As someone who serves on the Distinguished Review Board for the OWASP Agentic Top 10, and who spends every day thinking about how to secure agents across enterprise environments at Zenity, this distinction is not academic. It is potentially the single most important conceptual framework practitioners need to internalize right now.

This week’s McKinsey incident should be a wake-up call for every enterprise moving fast to deploy AI. Not because AI itself is inherently insecure. But because too many organizations are still thinking about AI security at the model layer, while the real enterprise risk sits in the action layer: the APIs, MCP servers, internal services, and shadow integrations that AI agents can reach, invoke, and manipulate. That is the part most companies still do not see.

Over the past year, AI-generated code has moved from novelty to normal. Developers are shipping faster, prototyping faster, refactoring faster… sometimes without fully understanding what they just merged. From the outside, it looks like a productivity renaissance. From the inside, it feels like something else: a new kind of operational risk that doesn’t behave like the old kind.

Last month, the Mexican government was hacked. 150GB of government data was stolen, including 195 million taxpayer records. This attack exploited a couple of dozen vulnerabilities across ten institutions. In the past, this would have likely taken a skilled team months to crack. But of course, we’re living in a new age. This attack was executed by one person and their Claude Code assistant.

Your CNAPP flags a misconfigured service account. Your CSPM warns about an overly permissive IAM role. Your container scanner reports vulnerabilities in a model-serving image. But none of these tools can tell you that an AI agent just called an internal admin API it has never touched before — or that a prompt injection caused your LLM to leak customer data through a RAG connector.

Your SOC gets three alerts in quick succession: an unusual outbound connection from a container, a file read on a Kubernetes service account token, and a process spawn that doesn’t match the workload’s baseline. Three different tools, three separate dashboards, three tickets.

Security teams are being asked to do more than ever, often with fewer people and less time. As alert volumes continue to rise and adversaries automate their attacks, even mature SOCs struggle to keep pace. Legacy tools surface signals, but they still leave analysts responsible for triage, investigation, and response decisions that take time and experience to execute well. CrowdStrike Charlotte AI was built to change that model.

Let’s start by drawing a strong distinction between what LimaCharlie does and what others offer in their AI SOCs. LimaCharlie's Agentic SecOps Workspace is an architecture that integrates AI as part of the security fabric. It's agentic AI security you own and control, not a black box you subscribe to. We introduce an easily deployable SOC-as-code approach that increases your control and capabilities.