Having spent the last quarter scrutinizing our software composition analysis (SCA) costs, our team's deep dive into Black Duck inevitably led us to evaluate its operational efficiency. The marketing, particularly around 'AI-powered triage,' was a significant factor in our selection. However, after several months of use, I must concur with the sentiment implied by the thread title: the so-called AI triage appears functionally indistinguishable from a sophisticated regex rules engine.
Our primary expectation was a system that learned from our previous triage decisions, contextually understood the libraries we use, and progressively reduced false positives and noise. What we observed instead was a pattern-matching system that flags issues based on predefined rulesets. For instance:
* It consistently flags certain common logging libraries in a vulnerable version range, despite those libraries being used in a non-exploitable context (e.g., internal admin tools with no external attack surface).
* It fails to correlate a library marked as "approved for use" in one project when it appears in another, forcing a manual re-triage.
* The "suppression" rules we create feel identical to writing complex regular expressions against component names and version patterns.
From a FinOps perspective, this inefficiency translates directly into measurable cost:
* **Engineering Hours**: Manual triage of what was promised to be auto-triaged issues consumes valuable sprint time.
* **Platform Cost**: The compute/storage overhead for processing every issue with what seems like a brute-force method, rather than an intelligent filtering agent, is non-trivial at scale.
* **Opportunity Cost**: Time spent on false positives is time not spent on genuine, high-risk vulnerabilities.
The promise of AI-driven efficiency suggested a tool that would get cheaper to operate over time as it learned. A static rules engine, however, maintains a linear or even increasing cost as your codebase grows. This is a critical consideration for any cloud cost watcher. Before procurement, teams should rigorously validate the "AI" claims against concrete, measurable reduction in manual effort, not just a flashy dashboard.
Optimize or die.
CloudCostHawk
This is a really valuable point, user250. The gap between expecting a system that learns from your context and getting one that just applies broad rules is a major pain point.
You've touched on something key: the "approved for use" flag not transferring between projects. That lack of organizational memory is a huge efficiency drain and exactly where a genuine learning system should shine. It suggests the "intelligence" is scoped very narrowly, maybe per-project, rather than building a company-wide profile. Did you ever get a clear answer from their support on whether that correlation is a planned feature, or if the design philosophy is intentionally siloed?
Keep it constructive.