Catch it in the pull request, not in the breach report.

AppSecD — AI-native application security across the whole development lifecycle

Catch it in the pull request, not in the breach report.

AppSecD runs SAST, DAST, SCA, secret detection, IaC, container, Kubernetes and API security from one platform — wired into Git, gated at the pull request, and managed through a real vulnerability lifecycle.

AppSecD is an enterprise application security platform that consolidates the scanning disciplines a security team would otherwise buy separately. Static analysis runs across more than twenty languages through a farm of over fifteen analyzers with Semgrep as the primary engine. Secret detection runs against both the working tree and the full git history, because a credential deleted in the latest commit is still in the repository. Software composition analysis covers the major package ecosystems and adds typosquat, malicious-package and provenance checks on top of conventional CVE matching. Dynamic testing brings a further one hundred and fifty native checks alongside Nuclei, Dalfox, SQLMap and out-of-band interaction testing. Findings are not dumped as raw scanner output: they are deduplicated across scans, tracked through a lifecycle with ageing, SLA, assignment and maker-checker approval, and enriched by an AI layer that triages false positives, proposes fixes and assembles attack chains. It integrates with GitHub and GitLab, gates pull requests by policy, and runs shift-left inside the developer IDE.

From the inside out: What are we shipping into production right now? Category: Application Security Posture Management (ASPM). The platform runs at https://appsecd.com.

What AppSecD covers

  • Static analysis (SAST) — Semgrep as primary engine plus Bandit, gosec, ESLint security, Brakeman, PHPStan/Psalm and SpotBugs with FindSecBugs — 20+ languages against OWASP Top 10, CWE Top 25 and custom rule packs.
  • Secrets across history — TruffleHog and Gitleaks, deduplicated, run over the working tree and the full git history — API keys, tokens, private keys, database and cloud credentials.
  • Supply chain (SCA) — Trivy and OSV across npm, PyPI, Go, Maven, RubyGems, Cargo, Packagist and NuGet, with typosquat detection, malicious-package detection, provenance checking and CISA KEV correlation.
  • Dynamic & API testing — Nuclei, Dalfox, SQLMap, Commix, Katana, Arjun, FFuf and Interactsh for out-of-band detection, plus 150+ native checks and API discovery across 50+ web frameworks.
  • Infrastructure & containers — Terraform, CloudFormation, Helm, Ansible and Pulumi rules; Dockerfile hardening with image CVE scanning; Kubernetes RBAC, network policy and pod security context analysis.
  • Vulnerability lifecycle — Deduplication across scans, new / reopened / fixed state, ageing and SLA, assignment, and maker-checker approval so a finding cannot be closed unilaterally.

The chain, step by step

  1. Threat modelling

    What happens: Most breaches trace back to a design decision nobody reviewed — a trust boundary that was never drawn, an authorisation model assumed rather than specified.

    How AppSecD answers it: Threat modelling starts before the code exists: components, trust boundaries and data flows are mapped so the controls that matter are identified while they are still cheap to add. (Threat modelling)

  2. Code and dependencies

    What happens: A developer writes a query that concatenates input, or pulls a package whose maintainer account was taken over last week.

    How AppSecD answers it: SAST across 20+ languages runs alongside SCA with typosquat, malicious-package and provenance checks — and secret detection reads the git history, not just the diff. (SAST · SCA · secrets)

  3. The pull request gate

    What happens: Without a gate, the finding becomes a ticket, the ticket becomes a backlog item, and the backlog item ships.

    How AppSecD answers it: Policy-driven gating blocks the pull request or commit when it introduces a violation, so the fix happens while the author still has the context. (PR / commit gating)

  4. Is it actually reachable?

    What happens: A team that treats every finding as equally urgent stops treating any of them as urgent.

    How AppSecD answers it: Dataflow taint analysis traces source to sink across functions, and CVE-reachability answers whether the vulnerable code path can be reached at all before anyone is paged. (Taint & reachability analysis)

  5. Exploit chain assembly

    What happens: Individually a medium and a low look ignorable. Chained together they are a path from unauthenticated request to data.

    How AppSecD answers it: The AI layer assembles attack chains across findings, showing how separately-unremarkable issues combine — and what the business impact of the chain is. (AI attack-chain analysis)

  6. Running application

    What happens: Code review cannot see a misconfigured production header, an exposed API route or an injection that only appears once the app is assembled and running.

    How AppSecD answers it: DAST and API-sweep test the deployed application: 150+ native checks with Nuclei, SQLMap and out-of-band interaction detection across 50+ frameworks. (DAST · API security)

  7. Closing the loop

    What happens: A finding that is found, ignored, re-found and re-ignored is worse than one never found — it consumes attention and buys nothing.

    How AppSecD answers it: Findings deduplicate across scans, carry ageing and SLA, get assigned to an owner, and need maker-checker approval to close. Security champions get a dashboard that tracks it. (Lifecycle · champions)

Inside AppSecD

  • One platform instead of seven tools — SAST, DAST, SCA, secrets, IaC, containers, Kubernetes and API security run from a single platform against a single finding model — so a vulnerability is deduplicated once and tracked once, no matter which engine found it. 15+ SAST analyzers, 20+ languages · 150+ native DAST checks · Secrets across full git history · One deduplicated finding model
  • Security intelligence mapped to your estate — An advisory only matters if you run the affected package. Every advisory is correlated against the versions, manifests and repositories actually present in your software estate, with CISA KEV flagged. 18 advisory feeds · Package-to-repository correlation · CISA KEV highlighting · Blast-radius view per CVE
  • Static analysis with reachability — Findings are ranked by whether the vulnerable path can actually be reached. Interprocedural taint analysis traces source to sink across function boundaries, and CVE-reachability answers the only question that matters. Taint analysis v1 and v2 · Interprocedural dataflow · CVE reachability · OWASP and CWE rule packs
  • A real vulnerability lifecycle — Findings deduplicate across scans and move through new, reopened and fixed. They carry ageing and an SLA, they get assigned, and closing one requires maker-checker approval. Deduplication across scans · SLA and ageing · Assignment and ownership · Maker-checker approval
  • Security champions, measured — Champion programmes fail when nobody can see whether they work. Teams get a posture leaderboard and per-team attribution, so improvement is visible rather than asserted. Team posture leaderboard · Per-team finding attribution · Clean-rate tracking · Champion enablement
  • AI that triages instead of guessing — 27 configurable AI features handle the work that consumes analyst time: false-positive triage, fix suggestion, attack-chain assembly, business-impact assessment and report summarisation. False-positive triage · Fix suggestion in context · Attack chain assembly · Business impact assessment

AppSecD at a glance

Category
Application Security Posture Management
Integrations
GitHub and GitLab, with PR and commit gating
Developer surface
VS Code, Cursor, Windsurf and JetBrains
API
Roughly 860 endpoints, OpenAPI documented

AppSecD questions

Does AppSecD replace our existing scanners?
It consolidates them. SAST, DAST, SCA, secrets, IaC, container, Kubernetes and API security all run from one platform against one deduplicated finding model, so the same vulnerability is not tracked three times in three tools.
How does it avoid burying developers in false positives?
Two ways. Reachability analysis determines whether a vulnerable path can actually be reached before anything is raised, and an AI triage layer labels likely false positives — with the label itself tracked, so triage quality is measurable.
Where does it fit in our pipeline?
It triggers on webhooks, scheduled runs, CI, ZIP upload or manually, and it gates pull requests and commits by policy. Developers also get results inside VS Code, Cursor, Windsurf and JetBrains before they push.
Can a developer close their own finding?
Not where you do not want them to. The lifecycle supports maker-checker approval, so a finding marked fixed or false-positive can require a second person to confirm it.

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