Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How I wrapped large-language-model power in a safety blanket of secrets-detection, chunking, and serverless scale.

Gain instant visibility into your cloud environment with Aikido Cloud Search. Search your cloud like a database. Whether you want to identify exposed databases, vulnerable virtual machines, or over-permissive IAM roles — Aikido gives you the power to uncover risk in seconds. No query language required, no waiting on devops. Just describe what you’re looking for, like “Give me all VMs with CVE-2025-32433 that have port 22 open.” Scroll down to "How It Works" to get technical.

In our last post, we explored how short-term memory enables agentic AI to hold a conversation that doesn’t reset after every message. That form of memory is all about flow—preserving context, user intent, and logic within a single session, even as interactions stretch across multiple turns. The longer the session, the more memory is required to maintain continuity. But not all memory needs to be verbose. Long-term memory serves a different purpose: persistence across sessions.

As part of the DevSecNext AI series, Jit hosted Sahar Carmel—Principal AI Engineer at Flare—for an inside look into what it really takes to become an “AI-first” developer. With nearly a decade of experience in AI and machine learning, Sahar has been hands-on with copilots and agents long before they were mainstream. In this session, he walks through his radical shift in workflow: from writing code line-by-line to orchestrating prompts, tokens, and memory banks.

Multi-agent systems (MAS) are reshaping industries from IT services to innovative city governance by enabling autonomous AI agents to collaborate, compete, and solve complex problems. This powerful transformation comes with a cost. As multi-agent systems grow, their risks also increase, opening the door to adversarial manipulation, emergent vulnerabilities, and distributed attack surfaces.