GitHub Advanced Security alternative — sandbox-verified + framework-aware
GHAS pattern-matches with CodeQL and ships Copilot Autofix in preview. Securie verifies every finding with a sandbox exploit and produces framework-aware fixes. Here's the comparison.
GitHub Advanced Security (GHAS) is the path of least resistance for teams already paying for GitHub Enterprise. Enable a billing flag, get CodeQL SAST, secret scanning with push protection, Dependabot, and Copilot Autofix in one dashboard. For an engineering leader who has to justify another vendor contract to procurement, 'just turn it on in GitHub' is a powerful default.
The friction shows up later. GHAS is built around the GitHub-centric view of a developer's world: code lives on GitHub, issues live on GitHub, security findings appear as GitHub issues on GitHub. When your production surface is Vercel, your data lives in Supabase, and your deploy target is a hosting platform GitHub does not directly observe, GHAS's coverage ends at the git push and leaves the most exploitable surface — the deployed application, its environment variables, its runtime authorization — outside its view. CodeQL is also a genuinely impressive pattern engine, but pattern engines do not prove exploits; they match shapes. On AI-generated code, shapes match constantly for bugs that are not real.
This page compares GHAS and Securie honestly for the class of team where GHAS's GitHub-centricity becomes a real constraint: teams shipping AI-built applications that cross the GitHub → Vercel → Supabase boundary and need proof-of-exploit rather than pattern matches. If your production is entirely inside GitHub (GitHub Actions workflows, GitHub-hosted artifacts, no external hosting), GHAS's scope maps cleanly to your surface and this page will say so.
Why people leave GitHub Advanced Security
- Only runs inside GitHub; your Vercel + Supabase surface is uncovered
- $49/committer/mo on top of GitHub Enterprise ($21/user/mo) — $70/user/mo total
- CodeQL is pattern-based; no exploit verification per finding
- Copilot Autofix is preview-quality; framework-awareness varies
Where GitHub Advanced Security actually breaks down
GHAS stops at the GitHub boundary
Example: A typical Securie customer ships through GitHub → Vercel → Supabase. GHAS scans the repository and emits alerts. It does not inspect the Vercel deployment's environment variables, cannot test the deployed URL against its own pattern matches, cannot verify whether a Supabase Row-Level-Security policy is actually enforced against a JWT attacker, and cannot block a deploy at the hosting layer if a regression slips through. The moment production leaves GitHub, so does the coverage.
Impact: The most exploitable parts of an AI-built application — the deployed runtime, the environment configuration, the database's effective access policy — are invisible to GHAS by design. Teams discover this during incident response ("GHAS showed clean; why did the breach happen?") because the incident happened at the Vercel or Supabase layer, not in the committed source.
CodeQL is a pattern engine, not an exploit engine
Example: CodeQL's query language is powerful for writing and sharing custom SAST queries, and its default query pack is well-maintained. But the underlying approach is static dataflow analysis over abstract syntax trees — it identifies code shapes that match a query. It does not execute the code, does not attempt to exploit the shape it identified, and cannot distinguish between a genuinely vulnerable route and a route that is shape-wise vulnerable but upstream-protected by middleware, feature flags, or network policy.
Impact: Developers receive 'High severity: potential SQL injection' alerts on routes that are provably safe in execution, and the severity label forces triage time. Real SQL injection bugs hide in the noise. The problem is architectural — it cannot be fixed by tuning CodeQL queries — because the question 'is this exploitable?' requires running the code, which CodeQL does not do.
Copilot Autofix is preview-quality, and the improvements are incremental
Example: Copilot Autofix has been in preview since October 2024 and ships fix suggestions on a subset of CodeQL findings. The suggestions are generated by a Copilot-class model conditioned on the CodeQL query and the code context. Quality varies: simple cases (add an input validation call, escape a template string) are reliable; framework-specific cases (the Supabase RLS policy needs `auth.uid()` not `auth.role() = 'authenticated'`) regularly produce plausible-looking but subtly wrong suggestions.
Impact: Developers either merge the suggestion and introduce a different bug, or distrust the suggestion and re-triage manually, which defeats the point. Because GHAS has no sandbox to verify the suggestion against a real exploit, there is no ground-truth feedback loop that improves the suggestion over time for your specific app.
Pricing is bundled behind GitHub Enterprise
Example: GHAS is $49 per active committer per month on top of GitHub Enterprise's $21 per user per month. An 'active committer' is any user who has pushed a commit to a protected branch in the last 90 days — this includes contractors, rotated developers, and anyone who merged a single PR during an open-source collaboration. The 90-day window surprises teams at renewal; the bill can be 20-40% higher than the headcount number they committed to procurement.
Impact: At 10 active committers, GHAS + GitHub Enterprise is $8,400/year. At 25 active committers (a typical Series-A team with contractors), $21,000/year. For teams at that size, the incremental cost of a dedicated security tool like Securie is often indistinguishable or lower, with materially better coverage on the deployed surface. The bundled pricing looks free-ish next to GitHub Enterprise until you actually compute it.
No Supabase, AI-feature, or framework-specific specialists
Example: GHAS's CodeQL queries are language-level — SQL injection in JavaScript, XSS in TypeScript, prototype pollution in Node. They are not framework-level. A Next.js Server Action that accepts unsanitized FormData and passes it to a shell command is a pattern CodeQL can catch at the exec() call, but the upstream Server-Action-specific checks (is the caller authenticated via middleware? does the FormData shape match the expected schema? is the function exported from a file under the right route group?) are not encoded. Similarly, Supabase RLS policies in SQL are not parsed; prompt injection in LLM tool calls is not modeled.
Impact: Teams ship AI-generated Next.js code that CodeQL misses because the bug lives at the framework-convention level rather than the language level. Securie's specialists are framework-native — a Supabase RLS specialist parses `CREATE POLICY`, a Next.js specialist models middleware → handler flow, a prompt-injection specialist models LLM tool-scope boundaries. That framework-native layer is the material difference on AI-built apps.
Why Securie instead
Covers beyond GitHub
Securie integrates with Vercel, Netlify, Cloudflare Pages, and the deployed-URL surface — not just source-code scanning.
Sandbox proof per finding
Every finding is reproduced as a working exploit before it reaches you. No CodeQL-style false positives.
Supabase + AI-feature specialists
GHAS has no Supabase RLS or AI-feature specialists; Securie does.
Free during early access
Zero billing vs $49/committer/mo for GHAS.
Feature matrix — GitHub Advanced Security vs Securie
| Area | GitHub Advanced Security | Securie |
|---|---|---|
| Finding verification | Pattern-based CodeQL dataflow; no exploit execution | Sandboxed exploit reproduction in Firecracker microVM per finding |
| Auto-fix quality | Copilot Autofix (preview); generic LLM suggestions | Framework-aware patch tested against the reproduced exploit; regression-verified |
| Scope beyond GitHub | GitHub-only; no Vercel, Supabase, or deployed-URL coverage | GitHub + Vercel Integration deploy-gate + Supabase RLS; full AI-app surface |
| Supabase RLS specialist | None | First-class; parses CREATE POLICY, models JWT-claim-to-row-visibility |
| BOLA / BFLA / IDOR | Generic CodeQL access-control queries; high FP on framework routes | Intent-graph-aware specialist distinguishing middleware-protected from actually-protected |
| AI-feature security | Not covered | Prompt injection, tool-scope abuse, RAG poisoning, jailbreak regression specialists |
| Secret scanning | Pattern + entropy; push-protection blocks commits | Live-validated against real providers; auto-rotate proposal opened as PR |
| Dependency scanning | Dependabot; mature across npm, PyPI, Maven, RubyGems, Go | Launch: malicious-npm detection + 15-min CVE-to-block; cross-language SCA roadmap |
| Deploy-gate enforcement | Pull-request status checks only; deploy not blocked at hosting | Vercel Integration blocks deploy at hosting layer before traffic arrives |
| Attestation / audit | Advanced Security audit log; no per-scan signed attestation | Signed in-toto + SLSA attestation per scan; auditor-consumable |
| Custom rules | CodeQL custom queries; powerful query language; steep learning curve | Managed framework-native specialists at launch; custom rules on Series-A roadmap |
| Pricing (10 active committers) | $4,900/yr GHAS + $2,520/yr GitHub Enterprise = $7,420/yr | Free during early access; founding-rate for life |
| Deployment modes | SaaS (GitHub-hosted); GitHub Enterprise Server for self-hosted | SaaS + Customer-VPC + TEE-native + on-prem air-gapped (Series A) |
The deeper tradeoff
GHAS's architectural bet is that security belongs inside the code-host. For organizations where the code-host is the center of engineering gravity — where code review, issue tracking, CI/CD, and deployment all live inside GitHub Actions and GitHub-hosted runners — this bet pays off. The friction is low, procurement is easy, and the feature set is broad enough that no additional vendor is needed for basic hygiene.
For organizations whose production surface spans GitHub + Vercel + Supabase + an AI inference provider, the bet starts to leak. GHAS does not observe what happens after the push. It cannot tell whether the Vercel deployment actually serves the patched code, whether the Supabase RLS policy is enforced against a real attacker JWT, or whether the environment variable containing the OpenAI API key has been read by the runtime. These are exactly the surfaces where AI-built applications break in 2026 — not at the pattern level in source, but at the runtime composition of framework + configuration + deployed policy.
CodeQL's underlying model also matters for AI-generated code specifically. AI models generate code that is plausible at the pattern level but wrong at the semantic level. A Next.js middleware file that looks like it checks authentication but actually runs only on a route segment the request never matches; a Supabase RLS policy that includes `auth.uid()` but in a position that does not actually filter rows; a Server Action that validates input with Zod but whose Zod schema is permissive. Pattern engines miss these because the pattern is correct — the semantic behaviour is wrong. Only executing the code reveals the gap, and only a sandbox can execute code safely against a malicious payload.
Copilot Autofix is a reasonable attempt at adding fix generation on top, but without an execution loop it cannot improve. The suggestion is generated, the human merges or rejects, the model does not learn per-app from the outcome. Securie's patch-and-verify loop closes this — the patch is tested against the exploit the sandbox already reproduced, and the loop iterates until the exploit fails. If GHAS is the 'pattern-match then suggest' model, Securie is the 'prove then fix' model. For AI-generated code the second model is materially more accurate.
The recommendation for teams currently paying for GHAS: do not cancel GHAS. CodeQL's secret-scanning and Dependabot are strong features and remain useful even alongside Securie. Add Securie for the specialist + sandbox + deploy-gate layer that GHAS structurally cannot provide. Re-evaluate at your next GHAS renewal once you have six months of parallel data. Many teams find Securie's deployed-surface coverage justifies keeping both; some find GHAS's role reduces enough that they downgrade to plain GitHub Enterprise (without Advanced Security) and save the GHAS premium.
Pricing
GHAS: $49/active-committer/mo + GitHub Enterprise ($21/user/mo). A 10-dev team: $8,400/year on GHAS alone. Securie: $0 during early access.
Migration path
- Keep GHAS enabled for breadth (it still catches generic patterns)
- Install Securie for the stack-specific bugs (RLS, BOLA, AI features)
- Compare weekly — most teams find Securie catches 3-5 real bugs per month GHAS misses
- Drop GHAS if your stack is Next.js + Supabase + Vercel (Securie covers it entirely)
Extended migration playbook
Step 1: Keep GHAS enabled; install Securie in parallel
What: Leave GHAS turned on in your GitHub Enterprise tier. Install the Securie GitHub App with access to the same repositories, plus the Vercel Integration if you deploy to Vercel.
Why: GHAS's strength — cross-language CodeQL, Dependabot, secret-scan push-protection — covers surface Securie does not yet own. Running both for the first quarter lets you compare and separates the 'GHAS is weak' argument from the 'Securie is ready' argument.
Gotchas: Both tools post check-runs on pull requests. Some teams get check-run fatigue; consolidate by configuring Securie's check as the primary and treating GHAS as informational until you have numbers.
Step 2: Capture the baseline: what does GHAS find, what does it miss?
What: For two weeks, on every merged PR, log: (a) GHAS findings that led to a code change, (b) Securie findings that led to a code change, (c) bugs caught post-merge (by users, by production incident, by manual review) that neither tool flagged.
Why: The comparison you want is not who flagged more, but who flagged what mattered. GHAS and Securie have different strengths; the question is whether Securie's strengths cover your actual incident profile.
Gotchas: Post-merge bugs are the hardest to attribute. Use a lightweight shared doc per incident: 'which tool should have caught this?' and be honest. If neither could have, record that too — some bugs require design review, not scanning.
Step 3: Consolidate based on your stack's shape
What: If your stack is purely Next.js + Supabase + Vercel + AI features, the data will typically show Securie catches the bugs GHAS misses (framework-specific, deployed-surface) while GHAS catches almost nothing Securie also misses. Reduce GHAS to Dependabot + secret-scanning only, saving the Advanced Security premium.
Why: GHAS Advanced Security is the expensive tier; Dependabot and secret-scanning are included in the base GitHub Enterprise. The consolidation is a pricing tier change, not a GitHub Enterprise cancellation — low-risk to execute.
Gotchas: Check your SOC 2 / compliance documentation. If you listed GHAS Advanced Security as a control, update the control description before downgrading to avoid audit drift.
Step 4: If you are a regulated / polyglot shop, keep both
What: If you run multiple languages in production (Python, Go, Java, Rust alongside TypeScript), keep GHAS for cross-language CodeQL. Securie covers the application layer for your TypeScript/Next.js slice; GHAS covers the other languages and the cross-language SCA.
Why: GHAS is still a strong pattern engine for non-AI-generated code and for languages Securie does not yet specialise in. There is no rush to consolidate; the tools are complementary in this profile.
Gotchas: Both tools use subscription seats. Review the 'active committer' definition in your GHAS contract — contractors who merged a single PR in the last 90 days still count. Right-sizing the GHAS license can save 15-25% at renewal.
Pick Securie if…
You want sandbox-verified findings + framework-aware auto-fix; your stack is AI-built.
Stay with GitHub Advanced Security if…
You need CodeQL custom queries for a regulated workflow, or your entire security practice is GitHub-standardized.
Common questions during evaluation
Does Copilot Autofix do the same thing as Securie's auto-fix?
Copilot Autofix generates fix suggestions on a subset of CodeQL findings using a Copilot-class model. It does not verify the suggestion against a reproduced exploit. Securie's auto-fix is generated, tested against the exploit the sandbox already reproduced, and iterated until the exploit fails — the patch has ground-truth validation that Copilot Autofix architecturally cannot match without a sandbox loop.
Can Securie replace Dependabot?
Partially at launch. Securie does malicious-npm-package detection and fast-CVE-blocking for npm within 15 minutes of disclosure — Dependabot takes 1-7 days for the same detection. For cross-language dependency scanning (PyPI, Maven, RubyGems, Cargo, Go modules) Dependabot is still broader; keep it for those languages until Securie's Series-A SCA ships.
How does Securie integrate with GitHub Actions?
Securie runs as a GitHub App, not a GitHub Action — scans trigger on every pull request opened, synchronized, or reopened, independent of your Actions workflow. Results post as PR check-runs and inline review comments with `suggestion` blocks you can merge in one tap. If you want to fail-fast on Securie findings, add a required status check on the Securie check-run in branch protection rules.
We use CodeQL custom queries for regulatory compliance — can Securie replace that?
Not yet. CodeQL custom queries are a first-class strength of GHAS, particularly for regulated workflows where the query itself is an audit artefact (you are saying 'we scan for exactly this pattern and here is the source'). Securie's custom-rule surface is on the Series-A roadmap. For now, keep CodeQL for the regulated query slice and add Securie for the framework-native + sandbox slice.
Does Securie cover GitHub Actions workflow security?
At launch, no — Actions workflow scanning (hardcoded secrets in YAML, pwn-request patterns, untrusted input flow) is a GHAS specialty. Securie's roadmap includes an Actions-security specialist in Series A. Until then, keep GHAS for your `.github/workflows/` scanning and use Securie for the application code.
What about GitHub Enterprise Server (self-hosted GitHub)?
Securie supports GitHub Enterprise Server via the GitHub App installed on your Enterprise Server instance. Scanning happens in Securie's SaaS (sealed-enclave) environment at launch; Customer-VPC deployment for air-gapped GitHub Enterprise Server customers is Series A.
Does Securie do attestation GHAS doesn't?
Yes. Securie emits a signed in-toto + SLSA attestation per scan, linking the repository state, the findings, the exploits proved, and the patches applied. GHAS provides an audit log but not a cryptographic attestation you can hand to an auditor or an insurer. This matters for SOC 2 Type II continuous-controls evidence and for cyber-insurance underwriting.
Is GHAS still worth keeping at all if we install Securie?
Yes, for two things: (1) secret-scanning push-protection — GHAS blocks commits containing detected secrets at git-push time, which is genuinely earlier than any PR-time scan; (2) Dependabot — broad cross-language dependency updates that Securie does not yet replace at launch. These two features do not require the full Advanced Security tier on most plans, so you can often downgrade GHAS rather than cancel it and keep the valuable parts.
Verdict
GitHub Advanced Security is the right default for teams whose surface is entirely inside GitHub — GitHub Actions workflows, GitHub-hosted runners, GitHub-deployed Pages — and who value the procurement simplicity of a single vendor. For this profile, GHAS covers most of what matters and Securie's incremental value is smaller.
For teams whose production crosses the GitHub → Vercel → Supabase → AI-inference boundary, GHAS's structural limit (GitHub-only, pattern-based, no sandbox) is a real gap, and Securie is purpose-built to fill it. The pragmatic path is to run both for a quarter, measure which tool caught which bugs, then consolidate — usually by downgrading GHAS to the base tier (keeping Dependabot and secret-scan push-protection) and letting Securie own the application-layer and deployed-surface coverage.
The false dichotomy in this market is 'GHAS or third-party SAST'. The useful framing is 'GHAS or Securie for your specific surface'. Map your surface to each tool's coverage and the answer is usually unambiguous in a week of parallel running.