Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If Gen AI adoption were a drinking game, most companies would be three rounds in and still adding shots. I mean, with a new LLM-powered feature every sprint, agents wired into internal APIs, RAG pipelines indexing everything from Confluence to the HR drive, i.e., fast, exciting, and almost nobody checking what happens when someone hands the model a sentence or a txt.file it wasn’t supposed to receive.

Every company has a version of the same thing. Sometimes it’s a security wiki. Sometimes it’s a Confluence page. Sometimes it’s a PDF nobody wants to update.

I recently wrote about how today’s cyber risk is defined less by breakthrough innovation and more by the industrialization of existing weaknesses. Given this, I wanted to dig a little deeper. Over a weekend I conducted some analysis on a longitudinal Aggregate Cyber Risk Index that scores six core threat vectors daily for 1,000 days on a 0–100 scale, drawing on six macro categories.

Accelerating security solutions for small businesses‍ Tagore offers strategic services to small businesses. A partnership that can scale‍ Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors‍ Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.

Security teams ingest massive volumes of telemetry from endpoints, cloud workloads, identity providers, and network controls. The goal is faster threat detection and shorter incident response times. But the reality is that all of this data becomes harder to move, slower to query, and messier to analyze as it grows. That's data gravity, and it's the biggest barrier to effective AI in cybersecurity.

For years, SOC analysts have lived in a world of swivel-chair analysis. When an alert fires in an endpoint tool, the next step is almost always a manual pivot to a network console to see if the network reality matches the host behavior. This manual back-and-forth isn't just tiring; it’s a window of opportunity for attackers. Corelight is excited to highlight a new integration with CrowdStrike Charlotte AI.

As part of our work at Tigera building products that create secure runtime environments for enterprise agents at scale in the real world, one small part of this puzzle I think about a lot is policy, and runtime enforcement of policy, and how to create a comprehensive secure runtime, configured from one place.