Industry Analysis
State of Vulnerability Management 2026 Report
Where vulnerability management actually stands in 2026: KEV-driven prioritization, reachability, SLAs that hold, and the tools teams are consolidating onto.
Feb 4, 20268 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Where vulnerability management actually stands in 2026: KEV-driven prioritization, reachability, SLAs that hold, and the tools teams are consolidating onto.
LLM spend forecasting is where finance teams meet AI engineering for the first time. The patterns that produce predictability are specific.
DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.
Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.
Engine work parallelises cleanly. Model calls do not. We explain why Griffin AI's throughput scales with CPU while Mythos-class tools bottleneck on rate limits.
RAG pipelines have six or seven supply chain surfaces, and most teams are only watching one. Here is how the attacks actually look in production.
AI-powered fuzzing and code analysis are accelerating zero-day discovery. Here's what that means for defenders.
Where the OCI and CNCF image supply chain ecosystem actually sits in 2026, what has stabilized, what is still contested, and what to deploy now versus later.
SEvenLLM set out to measure how well LLMs handle Security Event analysis, the unglamorous day-to-day work of SOCs and IR teams. A design review of what the benchmark covers, how it was built, and where the coverage maps or does not map to real operations.
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