Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

While traditional security tools excel at finding vulnerabilities, the sheer volume of alerts—now accelerated by AI-driven development—has made manual triage impossible. The true value of Application Security Posture Management (ASPM) lies not in providing more visibility or creating a cleaner backlog, but in shifting from cataloging risk to taking fast, context-driven, machine-speed action to actually fix what is broken.

AI agents can investigate a single vulnerability brilliantly, but that is only about 20% of vulnerability remediation. This post breaks down the other 80%: the data normalization, cross-tool asset identity, SLA enforcement, exception governance, and audit evidence that turn individual agent outputs into a governed, provable remediation program, and why AI and a platform like Seemplicity work better together than apart.

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.

In this exclusive fireside chat, Seemplicity CPO Ravid Circus and SANS instructor Jonathan Risto break down this critical distinction and why mastering it is vital as AI rapidly reshapes the cybersecurity threat landscape. Here’s a summary of what they covered. If you’ve been in security for any length of time, you’ve probably wondered whether exposure management is just vulnerability management with a fresh coat of paint.

AI is widely used in exposure management, but most implementations stop at prioritization and analysis. While AI improves visibility and decision-making, remediation still depends heavily on manual ownership, coordination, and inconsistent processes. To truly improve vulnerability remediation outcomes, AI needs to extend into the execution layer, helping identify owners, define remediation plans, and deliver fix-ready work that turns decisions into action.

Managing a bug bounty program often creates a significant operational burden because the findings tend to be unstructured and noisy compared to automated scans. This blog explains how to bridge the “triage gap” by using Seemplicity to transform free-form bug bounty data into a structured remediation workflow.

The cybersecurity industry is currently drowning in an “alphabet soup” of over 500 different category acronyms, a trend that is creating unnecessary noise and silos rather than helping practitioners. This hyper-niche branding often forces security teams to manage fragmented dashboards that don’t communicate with each other, adding to their workload instead of reducing it.

Anthropic’s Project Glasswing and the Claude Mythos model represents a fundamental change in the physics of cyber defense. With the gap between patch releases and weaponized exploits shrinking to hours, traditional manual security triage is now obsolete. Organizations must adopt AI-driven automated remediation.