Securie vs GitHub Advanced Security
GHAS bundles CodeQL SAST, secret scanning, and Dependabot under GitHub Enterprise. Securie is a standalone autonomous security engineer with sandbox-verified exploit proofs and auto-fix pull-request comments. Full feature and pricing breakdown.
The Securie-vs-GHAS comparison is different from the Securie-vs-Snyk comparison because GHAS is not an independent security tool — it is a feature bundle inside GitHub Enterprise. Teams comparing the two are almost always asking: 'we already pay for GitHub Enterprise, should we also add GHAS, or should we skip GHAS and install Securie directly?' This page is an honest answer to that specific question.
The GHAS value proposition rests on procurement simplicity and on CodeQL's query language. For organizations where adding a second vendor requires a formal security-review process, 'just turn on Advanced Security in GitHub' is a meaningful shortcut. For organizations where CodeQL custom queries are used as audit controls or as a research platform, the query language is a genuine advantage.
The GHAS weakness is scope. GHAS stops at the GitHub boundary — it scans source code, it scans CI/CD workflows, it protects git pushes. It does not observe what happens after the deploy, does not sandbox-verify findings, does not specialize in AI-built-app framework patterns, and does not produce signed per-scan attestations. For teams whose risk model crosses the GitHub → Vercel → Supabase → AI-inference boundary, GHAS's coverage is structurally narrower than the surface.
Securie is purpose-built for that crossed surface. This page walks the decision honestly: when GHAS is sufficient, when Securie is the better fit, and when running both is the right stack.
GHAS is a reasonable default if you are already paying for GitHub Enterprise and want basic SAST turned on without another purchase order. Its limits are noise (CodeQL is still a pattern engine) and scope (GitHub only, no runtime, no deploy gate). Securie is purpose-built for AI-generated code, runs across GitHub + Vercel, and produces a one-tap auto-fix PR on every finding.
Feature comparison
| Securie | GitHub Advanced Security | |
|---|---|---|
| Finding verification | Working exploit in sandboxed fork of your app | Pattern-based (CodeQL queries) |
| Auto-fix | Framework-aware patch, one-tap merge | Copilot Autofix (preview), suggestion only |
| Supabase RLS specialist | Yes, first-class | No |
| AI-feature security | Prompt injection, tool abuse, RAG poisoning | Not covered |
| Secret scanning | Live-validated, auto-rotate proposal | Pattern + entropy; validation via push protection |
| Deploy gate | Vercel Integration, blocks unsafe deploys | Pull-request status checks only |
| Runs outside GitHub | Vercel, Netlify (roadmap), GitLab (roadmap) | GitHub only |
| Compliance attestation | Signed per-scan attestation (in-toto + SLSA) | Advanced Security audit log |
| Language scope | TypeScript + JavaScript; Python roadmap | C/C++, C#, Go, Java, JavaScript, Python, Ruby, Swift |
Where the difference shows up in practice
A Next.js middleware that appears to protect /api/admin/* but matches a prefix that excludes /api/admin/v2/*
GitHub Advanced Security: CodeQL's dataflow analysis reads the middleware definition and matches it against routes. The matcher config is a string; CodeQL does not have middleware-routing semantics and treats the match as literal. The bug — matcher is '/api/admin' without trailing wildcard, so /api/admin/v2/users is not matched — is invisible to CodeQL.
Securie: Securie's Next.js specialist parses middleware matchers with Next.js App Router semantics and identifies that the admin namespace has child routes not covered by the matcher. The sandbox attempts an unauthenticated request to /api/admin/v2/users, observes 200 OK, and ships the finding with the repro + a patch that fixes the matcher config.
A leaked GitHub Personal Access Token committed in a test file
GitHub Advanced Security: GHAS secret-scanning with push-protection blocks the commit at git-push time — this is actually a genuine GHAS strength, earlier in the workflow than any PR-time scan. The token is caught before it enters the repository.
Securie: Securie's secret scanning catches the token if it lands in the repo (not as early as GHAS's push-protection). Where Securie adds value is live-validation: if the token is already committed (pre-existing leak discovered during scan), Securie validates whether the token is still active against the GitHub API and opens an auto-revoke + rotate PR. GHAS flags without validation or rotation.
A CodeQL custom query an auditor requires for a specific regulated pattern
GitHub Advanced Security: CodeQL custom queries are a first-class GHAS feature. Write the query in CodeQL's declarative query language, commit it to .github/codeql/custom/, GHAS runs it on every scan. The query source is auditable evidence that the specific pattern is being scanned for.
Securie: Securie's launch specialist fleet is managed and framework-native; custom rule authoring is Series-A roadmap. For regulated workflows where the custom query is part of an audit control, keeping GHAS for that specific control is the right answer — Securie does not yet replicate this surface.
A deploy to Vercel with an env var leaking a Supabase service-role key
GitHub Advanced Security: GHAS does not observe Vercel deploys. The env-var configuration lives in Vercel's dashboard; GitHub has no view of it. The leaked key is invisible to GHAS unless it also appears in source code, which service-role keys typically do not.
Securie: Securie's Vercel Integration reads the deploy-time env-var configuration and flags service-role keys in locations where they could be accessed from client-side code (NEXT_PUBLIC_* prefixed, exposed through getServerSideProps patterns, shipped in middleware that runs in the browser context). The deploy-gate blocks before traffic arrives, and an auto-rotate PR proposes moving the key to a server-only env var.
The deeper tradeoff
GHAS's architectural model is 'security as a feature of the code host'. Code lives on GitHub; code review happens on GitHub; security findings appear on GitHub; everything stays in one place. This model is elegant for teams whose engineering reality is GitHub-centric and whose security risk is well-approximated by pattern-matching over committed code.
AI-built applications in 2026 do not fit this model cleanly. The engineering surface spans GitHub (code), Vercel (deploy target and runtime), Supabase (auth + database + policies), and an AI inference provider (OpenAI, Anthropic, Gemini). Each surface has its own security semantics, and the bugs that cause incidents often live at the seam between surfaces — an environment variable leaked from Vercel, a Supabase RLS policy that is enforced at Supabase but whose pattern is invisible to GitHub, a prompt-injection vulnerability that exists at the inference layer where GitHub cannot see the call pattern.
Securie extends the security surface across these seams. The Vercel Integration gives Securie deploy-gate control; the Supabase specialist parses CREATE POLICY statements with full Supabase semantics; the AI-feature specialists read the code but verify against reproduced LLM calls. This is a different architectural bet than GHAS — not competing at the same layer, but extending coverage to the layers GHAS structurally cannot reach.
The second architectural difference is verification. GHAS's CodeQL is a powerful pattern engine, but it is a pattern engine — it does not execute code. Pattern matches on AI-generated code, which looks correct at the static level and is wrong at the execution level, produce noise that GHAS cannot filter. Copilot Autofix attempts to close the fix-generation loop but without execution, it cannot verify its own output. The suggestions are plausible and sometimes wrong.
Securie's sandbox is the verification primitive. For every finding, the sandbox reproduces a working exploit or the finding is dropped. This produces zero-false-positive ticketing, ground-truth-tested patches, and attestation artifacts that are auditor-consumable without further work. The tradeoff is that Securie covers narrower language/framework scope at launch (TypeScript + JavaScript on Next.js + Supabase + Vercel); GHAS's CodeQL coverage is multi-language.
The honest recommendation for most teams is to run both during the first evaluation quarter, measure which tool catches which bugs, and consolidate based on the data. Many teams land on: keep GHAS's Dependabot + secret-scan push-protection (the base GitHub Enterprise features, not the full Advanced Security tier); consolidate SAST + deploy-gate + attestation on Securie; save the GHAS Advanced Security premium. Some teams keep full GHAS + Securie because the Dependabot + CodeQL breadth is genuinely valuable on top of Securie's depth. Either path is defensible; the wrong path is 'GHAS alone' for an AI-built-app team because the coverage is structurally insufficient.
Pricing
Free during early access. No credit card.
$49 per active committer per month, atop GitHub Enterprise ($21/user/mo)
Migration playbook
Step 1: Keep GHAS running; install Securie in parallel
What: Leave GHAS turned on in your GitHub Enterprise tier. Install Securie's GitHub App + Vercel Integration on the same repositories.
Why: GHAS's breadth (Dependabot, CodeQL multi-language, push-protection) covers surface Securie does not yet own. Running both for the first quarter separates 'GHAS is weak' from 'Securie is ready' and gives you data.
Gotchas: Both tools post check-runs on PRs. Configure branch-protection required-status-checks thoughtfully — typically Securie becomes the blocking check once you have data; GHAS remains informational during the evaluation window.
Step 2: Establish the baseline per surface
What: For two weeks, on every merged PR, log which tool flagged what. Categories: (a) GHAS caught, Securie missed; (b) Securie caught, GHAS missed; (c) both caught; (d) neither caught (post-merge incident). The mix tells you where coverage overlaps and where it complements.
Why: Most teams discover the overlap is narrow. Securie tends to catch framework-specific bugs GHAS misses; GHAS tends to catch cross-language patterns Securie does not yet cover. The tools are largely non-overlapping in scope.
Gotchas: 'Neither caught' is the most important bucket but the hardest to populate — it requires post-merge incident discovery. A third-party pentest during the window is a cheap way to surface this bucket.
Step 3: Consolidate based on stack shape
What: If your stack is purely Next.js + Supabase + Vercel (AI-built TypeScript), the data typically shows Securie catches what matters and GHAS's incremental catches are mostly dependency updates Dependabot already handles. Reduce to base GitHub Enterprise (keeping Dependabot + secret-push-protection) and save the Advanced Security premium. If your stack includes .NET, Go, or Java alongside, keep GHAS for cross-language CodeQL. Securie plus GHAS covers your full surface with clear category ownership.
Why: The consolidation question is 'which premium features of GHAS are you actually consuming?' If the answer is 'Dependabot, which is base Enterprise anyway', the premium is shelfware. If the answer is 'CodeQL custom queries for Java', the premium earns its cost.
Gotchas: Compliance documentation: if you listed GHAS Advanced Security as a SOC 2 control, update the control description before downgrading. Auditors catch stale control descriptions and it creates friction at the next audit.
Step 4: Keep the Dependabot / push-protection layer
What: Dependabot and secret-scanning push-protection are included in base GitHub Enterprise at no additional cost. Leave them enabled — they catch things earlier in the workflow than any PR-time scan (push-protection blocks the commit; Dependabot auto-updates).
Why: This is defense-in-depth at no incremental cost. Securie is advantaged on precision and depth; GHAS base features are advantaged on timing (catching issues before they enter the repository).
Gotchas: Dependabot's PRs can become noisy if left unconfigured. Set up auto-merge for patch-level updates and require review for minor/major updates; this balances hygiene with signal.
When to pick GitHub Advanced Security
Your org already standardises on GitHub Enterprise, you do not ship outside GitHub, and you want a default-on SAST that does not require another vendor contract.
When to pick Securie
You build AI apps, your stack is Next.js + Supabase + Vercel, and you want fixes in your PR rather than CodeQL queries in a dashboard.
Bottom line
Pick GHAS if you are deeply committed to the GitHub platform and your team is comfortable triaging pattern-matched findings. Pick Securie if you want sandbox-verified proofs, framework-aware auto-fixes, and coverage that follows your app from GitHub through Vercel to production.
FAQ
Can Securie run alongside GHAS?
Yes. Many teams keep GHAS enabled for broad-spectrum CodeQL coverage and add Securie for the stack-specific findings (Supabase RLS, broken access control, AI-feature bugs) where precision and auto-fix matter most.
Does Copilot Autofix do the same thing?
Copilot Autofix proposes fixes for a subset of CodeQL findings in preview. It does not verify findings via a sandbox. Securie runs the exploit before emitting any patch, and the patch is framework-aware rather than generic.
What about GHAS Dependabot — do I still need it?
Dependabot is part of GitHub Enterprise, not GHAS specifically — you retain it if you downgrade from GHAS to base GitHub Enterprise. For teams that consolidate on Securie for the SAST + deploy-gate + attestation layer, keeping Dependabot for cross-language dependency updates is sensible because Securie's launch SCA scope is npm-only.
Does Securie integrate with GitHub Advanced Security's native alert UI?
At launch, Securie's findings land in PR review comments and in the Securie dashboard rather than in GHAS's Security tab. Integration with GitHub's native security-alert UI is on the Series-A roadmap. For teams that want all security findings in one GitHub-native surface, running both tools and accepting dual dashboards is the current pattern.
What happens to CodeQL custom queries if we move away from GHAS?
CodeQL custom queries are a GHAS feature — they run against a CodeQL database. If you move off GHAS, the custom queries stop running unless you run them via the CodeQL CLI yourself. For regulated workflows where the custom query IS the audit control, keeping GHAS for that narrow slice is often the right answer.