Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If you run engineering, security, or compliance at an Indian tech company, DPDP compliance is knocking at your door fresh and clean in less than a year. Our aim is not to present scary statistics but to help you recognize the urgency of the matter and become DPDP compliant at the earliest. Since this law safeguards a nation’s data, the DPBI can thus stack penalties across multiple contraventions in a single incident. So stop debating whether the law applies to you; it almost certainly does.

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.

For years, application security ran on a simple assumption: software moves through a lifecycle, and security inspects the artifacts as they travel from development to production. Developers plan, write code, commit it, test it, scan it, and ship it. Every control built, including pull request reviews, CI/CD gates, and post-commit scanning, assumed a human was sitting between each step, making decisions a tool could later check.

Security budgets have never been higher. The average enterprise now runs 50 security tools, and most teams added more last year than the year before. And yet, alert fatigue is at the breaking point. Coverage gaps in mobile and API environments continue to widen. The exploitability problem at the center of most AppSec programs remains unsolved. Breaches keep happening. Risk scores don't move.

The security industry hasn’t been wrong about agentic AI risk. It’s been incomplete. There’s no shortage of single-signal solutions for the problem: tools that analyze prompts for malicious content, platforms that monitor data access patterns, capabilities that assess model behavior for signs of manipulation. Each captures something real. None is sufficient on its own.

In previous Strategic CISOs sessions, I’ve spoken with security leaders from Andesite, IMO Health, and Cribl. They’ve built trusted programs where GRC functions as a business driver and customer assurance accelerates revenue. But every CISO I speak with is still fighting some version of the same fight. They have more obligations, more scrutiny, and more AI-related risk, but they do not have more people, more budget, or more hours in the day.

Two things shipped this week. One is for the MSPs who manage certificates on behalf of other companies. The other lets you run the shorter 45-day renewal cycle today.

Seemplicity’s new EDR Compensating Controls Awareness feature reduces vulnerability backlogs by embedding live, asset-level endpoint telemetry directly into remediation workflows. By automatically mapping EDR policy configurations against specific CVE attack techniques, the platform determines if an active endpoint control already neutralizes a threat. Each finding is dynamically assigned a clear protection outcome, complete with an auditable evidence trail.

The pitch is familiar enough that most security leaders tune it out. It sounds like marketing language, just an updated way of saying “a better scanner.” This post is here to bust the myth behind that framing. Both scanners and autonomous pentesting agents look the same from the outside. Both crawl your application, both send payloads, and both produce findings. But they operate on completely different assumptions of what constitutes a vulnerability.