Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Snyk empowers organizations to build fast and stay secure. As security and engineering teams scale their use of Snyk across the enterprise, understanding what's happening across your group and organizations becomes critical–from API integrations and user access patterns to policy changes and security events. However, raw audit logs alone can be overwhelming and difficult to interpret. Security leaders need instant visibility into critical events, risk patterns, and user activity.

In finance, protecting customer data isn’t just good practice. It’s a regulatory mandate. The SEC’s Regulation S-P (Privacy of Consumer Financial Information) requires financial firms to guard against unauthorized access, maintain robust data-disposal practices, and have a formal incident response program. As the threat landscape has evolved, so has the regulation. This all means one thing: complacency is no longer an option.

It’s a bold claim. And sometimes customers ask me, “What exactly do you mean by important?” That’s a fair question because importance isn’t about how complicated a workflow is, or how many tools it touches. It comes down to impact.

As enterprises embrace agentic AI, a new security risk equation emerges. In this episode of Security Matters, host David Puner sits down with Lavi Lazarovitz, VP of Cyber Research at CyberArk Labs, to unpack how AI agents and identity security are reshaping the threat landscape. Learn why privileged access is now the fault line of enterprise security, how attackers exploit overprivileged AI agents, and what security teams must rethink before scaling AI.

It starts with a simple audit. Your legal team checks Business Associate Agreements after OCR’s tracking technology guidance. Google Workspace BAA: signed. Analytics platform BAA: signed. CRM and marketing tools: covered. Then the question that changes everything: Do we have BAAs for the tracking pixels on our patient pages?

You don’t have to dig far into a payment page before the pattern makes itself obvious. A few scripts are yours. A few belong to vendors you expected. But then the list keeps going, and you start to see something else.

For many DevOps and ITSM workflows, Jira is the nerve center. It’s relied upon by thousands of teams for everything from agile sprint planning to enterprise-scale incident management. However, beneath the robust interface and powerful automation, your Jira data remains fragile – far more than you think. Scenarions around Jira data loss aren’t a theory. At least nowadays, when such things happen it’s quickly and quietly.

A CI/CD pipeline deploying to production. A nightly database backup job. An AI agent performing maintenance tasks. New opportunities for engineering automation emerge every day. However, many of these workflows depend on stored secrets like hardcoded credentials, API keys, and long-lived tokens for privileged access.

The merits of deploying offensive testing to strengthen an organization’s security posture are well-understood by today’s security leadership. Much to the relief of defenders, obtaining approval for an offensive security exercise has never been easier. However, the process of selecting the most appropriate offensive testing solution requires untangling overlapping definitions and vaguely defined terminology that leaves security teams more confused than when they started.