Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

What's Actually Changing in HIPAA (and What IT Teams Need to Fix Now)

Hosted by CalCom — helping healthcare and financial organizations secure and harden server environments for compliance frameworks like HIPAA, CIS, and NIST. Trusted by organizations managing regulated environments where configuration security and audit readiness matter. A HIPAA Auditor won't tell your servers to turn their head an cough, but your configurations may still not be healthy. Some security practices that used to be “recommended” are quickly becoming expected controls. This session breaks down what’s changing, what auditors will look for, and what you should address now.

A Look At GitGuardian's ML-Powered Contextual EnrichmentAnd Incident Scoring

In this quick introductory video, Mathieu Bellon, Senior Product Manager at GitGuardian, sits down with Dwayne McDaniel, Developer Advocate, to cover some of the advancements GitGuardian has made by integrating machine learning directly into the secrets security platform. Mathieu describes how engineers and responders can save serious time as by automating contextual analysis, geving the humans in the loop with the best information to be able to take an informed action when it comes to secrets leaks. They also discuss the security implications and where teams can look if they want to opt out or bring their own agents.

How GitGuardian and CyberArk MCP Servers Cut Secrets & Vault Sprawl with AI Automation

Watch the teams of GitGuardian and CyberArk for a demo-first session on how MCP (Model Context Protocol) servers can help you tame secrets sprawl and vault sprawl by letting developers use AI to trigger the right actions, with far less cognitive load! What you’ll learn.

I Tried 5 Prompt Injection Attacks (Here's What Happened)

In this video, we explore the growing security risk of prompt injection in large language model (LLM) applications. As AI becomes embedded in more products, new vulnerabilities emerge, especially through natural language manipulation. We break down how LLMs work, the importance of system prompts, and demonstrate five real-world prompt injection techniques used to extract sensitive information or bypass safeguards. You’ll see live examples using different models and learn why newer models are more resilient, but still not immune.

CVE-2026-0968: The libssh Heap Read That Isn't as Scary as Scanners Say

A missing null check in libssh’s SFTP directory listing code lets a malicious server crash clients, but real-world exploitability is extremely constrained. CVE-2026-0968 is an out-of-bounds heap read in sftp_parse_longname(), triggered when an SFTP client processes a crafted SSH_FXP_NAME response with a malformed longname field. Red Hat, which serves as the CNA (CVE Numbering Authority) for this vulnerability, scored it 3.1 (Low), while Amazon Linux independently scored it 4.2 (Medium).

AI Workload Baseline and Drift Detection: Defining "Normal" Agent Behavior

Security teams deploying AI agents into Kubernetes know they need behavioral baselines. The concept is straightforward: define what “normal” looks like for each agent, then detect when behavior drifts in ways that suggest compromise. The problem is that AI agents are designed to change. A model update alters inference latency. A prompt revision shifts tool-calling sequences. A new MCP integration adds API destinations nobody flagged during the last security review.

How to Triage an AI Agent Execution Graph: A Three-Tier Decision Framework for Security Teams

A platform security engineer gets an alert at 2:14 a.m. One of the LangChain agents running in their production Kubernetes cluster has produced an execution graph with eleven nodes, seven tool calls, and an egress edge to a domain that is not in the agent’s approved integration list. The chain is fully rendered in their console. Every signal is there.

The CISO's AI Agent Production Approval Checklist: 7 Gates to Clear Before Go-Live

Your engineering lead is in your office Thursday morning. They want to push an AI agent to production next Tuesday. It’s a LangChain-based workflow agent, connected through MCP to three internal tools and one external API, with access to a customer database. The framework posters are on the wall. Your team has spent two quarters standing up runtime observability. And sitting in that chair, you still don’t know whether to say yes.