A few months ago, someone asked me what I was building. I said “an ASPM platform” and watched their eyes glaze over. So I tried again: “a tool that helps developers actually fix security issues instead of just finding more of them.” That landed better.
But the real answer is more personal than either of those.
The Problem I Kept Running Into
Over the past decade, I’ve worked across telecoms, fintech, and cloud-native startups — doing penetration tests, running DevSecOps transformations, and trying to build security into software delivery pipelines. The specific technologies changed. The core problem didn’t.
Every organization I worked with had the same story: they had tools. Lots of them. A SAST scanner, an SCA tool, maybe a container scanner, maybe something checking their IaC. And every single one of them produced a backlog so large it became meaningless. Hundreds of findings. Thousands. Engineers would look at the dashboard, feel overwhelmed, and go back to shipping features. Security would chase them. Nothing would get fixed.
I watched good security teams burn out trying to triage an endless stream of low-signal alerts. I watched engineering teams dismiss security findings entirely because the signal-to-noise ratio was so bad that they’d stopped trusting the tools. I was the person in the room trying to explain why this particular critical finding from six months ago actually mattered, while the team pointed at 847 other “critical” findings no one had touched.
This isn’t a tooling gap — it’s a prioritization and context gap.
What’s Actually Broken
The security scanning ecosystem has done a remarkable job of finding more things. SAST tools got smarter. SCA databases got more comprehensive. We added secret scanning, IaC scanning, DAST, container scanning. And yet the fundamental developer experience got worse, not better.
Here’s why: finding vulnerabilities and fixing vulnerabilities are completely different problems. The first is a detection problem. The second is a prioritization, context, and workflow problem. The industry invested almost entirely in the first and left the second mostly unsolved.
The result: a finding that says “CVE-2021-XXXX in library X” tells a developer almost nothing actionable. Is this library reachable from a public endpoint? Is there a known exploit in the wild? Does the CVE actually apply to how we’re using this library? Does fixing it break something else? Without answers to these questions, every vulnerability looks the same — and so nothing gets prioritized.
The Decision to Build
I started sketching out what a better tool would look like during a period where I was consulting for a mid-sized fintech and watching exactly this pattern play out in real time. Their security backlog had grown to over 2,000 findings. Their security engineer was spending most of their week in triage. Their developers had disabled the scanner notifications entirely.
What they needed wasn’t another scanner. They needed something that could take all of that noise, apply real context — reachability, exploitability data, business impact — and surface the 20 findings that actually mattered this sprint. And then, crucially, help the developer understand and fix those 20 things without needing a security expert sitting next to them.
That’s what I set out to build with ScanDog.
What ScanDog Does
ScanDog is an Application Security Posture Management (ASPM) platform. At its core, it connects to your existing security tools and repositories and does three things:
1. Consolidates findings across your entire stack. SAST, SCA, IaC, container scanning, secret scanning — centralized, deduplicated, normalized.
2. Prioritizes ruthlessly. Instead of presenting 2,000 findings, it applies reachability analysis, EPSS scoring, CISA’s Known Exploited Vulnerabilities catalog, and business impact context to surface the small percentage of findings that are genuinely urgent. Not everything is critical — and tools that treat everything as critical are lying to you.
3. Makes fixing easy for developers. Security findings land as contextual comments in pull requests. The AI-powered remediation layer explains what the issue is, why it matters in your specific context, and suggests a minimal, safe fix. You don’t need to be a security expert to act on it.
The guiding principle: security findings should be something a developer can act on, not something they need to escalate to understand.
Why Now
The timing isn’t coincidental. Two things have converged that make this the right moment.
First, AI code generation has accelerated the rate at which new code — and new vulnerabilities — enter codebases. The attack surface is growing faster than security teams can keep up with manually. Automation that actually reduces work (rather than just creating more dashboards) has gone from nice-to-have to necessary.
Second, LLMs are finally good enough to provide genuinely useful, context-aware remediation guidance — not just generic advice, but specific suggestions tied to your actual code, your actual dependencies, your actual risk profile.
Where It’s Going
ScanDog is early. I’m building it openly, drawing on everything I’ve learned from a decade on the security side of software delivery — and on what I’ve seen fail in the tools I’ve used and built alongside. The goal is a platform where security posture is something your team actively improves over time, not a checkbox you revisit once a quarter.
If you’re dealing with the same problem — too much noise, not enough action — I’d love to hear about your experience. You can find ScanDog at scandog.io.