Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The EU AI Act places significant emphasis on documentation because regulatory oversight depends on an organization's ability to demonstrate how its AI systems operate and how associated risks are managed. Compliance is not determined solely by how an AI system performs, but by whether the organization can provide evidence that appropriate governance, risk controls, and oversight mechanisms are in place throughout the system lifecycle.

Forget the breach notification email. Forget the security audit trail. A fintech user opened their chatbot last year, saw someone else’s account details staring back at them, and filed a support ticket. That’s how the team found out their LLM had been leaking customer PII for weeks. LLM data security isn’t a checkbox. It’s an architecture decision. Make it before the first model call, not after the first breach. Most teams get one expensive lesson before they understand that.

According to the SACR 2025 AI SOC Market Landscape report, 40% of security alerts go uninvestigated. The average alert investigation takes 70 minutes. Meanwhile, attackers achieve breakout in under 48 hours. That math doesn’t work in anyone’s favor — except the adversary’s.

Cloud adoption is accelerating, but cloud security complexity is growing just as fast. Security teams now manage hybrid workloads, multi-cloud environments, containerized applications, and sensitive cloud-native data. Traditional tools designed for on-prem environments often struggle to provide consistent visibility across these dynamic systems. This creates operational pressure. Teams deal with fragmented alerts, inconsistent policies, and uncertainty about real cloud risk exposure.

Continuous Threat Exposure Management is a continuous security framework for identifying, assessing, validating, and reducing the exposures that matter most to an organization. Rather than treating every exposure, alert, or control issue as equally urgent, CTEM helps organizations focus on the exposures that are actually reachable, relevant to likely attack paths, and meaningful in a business context.

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.

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.

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.

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.

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.