Securie vs CodeQL
CodeQL is GitHub's open-source semantic code analysis engine — you author queries in QL or use prebuilt query packs to find security bugs across a code-as-data graph. Securie is a sandbox-verified findings platform with framework-aware specialists. This page is the honest comparison of two structurally different approaches.
Teams comparing Securie and CodeQL are usually weighing flexibility against operational simplicity. CodeQL's flexibility is real and unique — the code-as-data semantic graph + QL query language is a genuinely powerful tool when wielded by an engineer comfortable with it. The cost is the engineering investment: authoring queries, maintaining them as the codebase evolves, running the scanning infrastructure, integrating findings into the development workflow.
Securie's specialists ship out of the box with their detection logic baked in. The Day-1 production-validated specialists (Supabase RLS, leaked secrets, broken auth) cover the highest-frequency risks for AI-built apps. The trade is flexibility — Securie does not currently support custom-query authoring at the same level CodeQL does. For org-specific business-logic coverage that does not match the specialist surface, CodeQL's flexibility wins.
The procurement reality is that CodeQL is a tool for teams with engineering investment in security; Securie is a product for teams who want security as a service. Both are honest positions, and the right one depends on team shape and priorities.
CodeQL is an open-source query engine — write QL queries against your code's semantic graph to find bugs. The strength is precision when the right query exists; the cost is authoring and maintaining queries. Securie ships framework-aware specialists with sandbox verification — every finding is a reproduced exploit. Pick CodeQL if your team has QL expertise and wants control over the query surface. Pick Securie if you want sandbox-verified findings out of the box without authoring queries.
Feature comparison
| Securie | CodeQL | |
|---|---|---|
| Detection model | Framework-aware specialist agents + sandbox verification of every finding | Code-as-data semantic graph + author-or-use QL queries that run against the graph |
| Out-of-box coverage | Day-1 production-validated specialists (Supabase RLS, secrets, broken auth) + ~20 code-complete | GitHub-maintained query packs covering common security patterns; coverage depth varies by language + by query pack |
| Query-authoring requirement | None — Securie's specialists ship with their detection logic baked in | QL is a real programming language; teams typically need engineers comfortable with it to extend the query surface beyond the GitHub-provided packs |
| Finding verification | Sandbox-replayed exploit per finding | Pattern-precise per the QL query; no runtime sandbox replay |
| False-positive rate | Zero by construction — no exploit, no ticket | Lower than regex-based SAST when the QL query is well-tuned; higher when queries are generic |
| Framework-specific specialists | Yes — Supabase RLS specialist, BOLA/BFLA specialist, AI-feature stack | Generic SQL injection / XSS / SSRF queries available; framework-specific queries require authoring |
| Auto-fix PR | Yes — sandbox-regression-tested patches | No — CodeQL produces findings; fix-generation is out of scope |
| Audit attestation | Signed in-toto + DSSE + Sigstore-rekor (Ed25519, KMS-backed in production) | SARIF findings export; GitHub Advanced Security (paid) provides workflow integration; not cryptographically attested per scan |
| License | Apache 2.0 platform; OSS model stack (Foundation-Sec / GLM / DeepSeek / Kimi) | CodeQL CLI free for open-source; GitHub Advanced Security required for private-repo auto-scan + dashboard |
| Pricing | Free / $12 / $49 / $299 / Enterprise capped-envelope monthly | OSS CodeQL free; GitHub Advanced Security ~$49/active committer/month // TODO: verify GHAS pricing |
Where the difference shows up in practice
A canonical SQL injection in a parameterized-looking but actually-string-concatenated query
CodeQL: CodeQL's GitHub-maintained query pack includes SQL-injection queries that catch the canonical pattern. The finding ships with the query name + the line + the path through the data flow.
Securie: Securie's specialist detects the SQL-injection candidate. The sandbox seeds a Postgres instance and replays the attack — confirms exploit succeeds. Finding ships with the reproduced exploit + the fix as a regression-tested patch. Both tools catch the canonical case; Securie adds the runtime verification + fix.
A Supabase RLS policy with USING but missing WITH CHECK on INSERT
CodeQL: CodeQL's default query packs do not include Supabase-specific RLS semantic queries. A team-authored QL query targeting CREATE POLICY statements with the USING-but-no-WITH-CHECK shape would catch it; the team has to write the query.
Securie: Securie's Day-1 production-validated Supabase RLS specialist parses the policy with full Supabase semantics + sandbox-replays the cross-user INSERT + ships the finding with the reproduced exploit. Out of the box; no QL authoring required.
An org-specific business-logic bug — internal API convention requires a tenant-id header that some routes do not check
CodeQL: A team-authored QL query encodes the convention: every route handler under /api/* must read the X-Tenant-Id header before any database call. The query runs and finds the missing-check routes. CodeQL's flexibility shines for org-specific patterns.
Securie: Out of scope at launch. Securie's specialists target framework-aware risk surfaces, not org-specific conventions. For this scenario, CodeQL's flexibility is the better fit; running both tools is the answer.
A leaked OpenAI API key in source code
CodeQL: CodeQL's secret-scanning queries catch the OpenAI key pattern. The finding ships in SARIF + GitHub PR. CodeQL does not live-validate against the OpenAI API.
Securie: Securie's secret_scanner specialist detects + live-validates against the OpenAI API + opens an auto-rotation PR (Indie tier and up). Validated exposure + closed-loop response. CodeQL's secret-scanning is alert-only.
The deeper tradeoff
CodeQL and Securie represent two structurally different approaches to code security. CodeQL is a query engine — your team writes QL queries against the code-as-data semantic graph, and the queries return findings. The strength is flexibility and precision: a well-authored QL query can find a class of bugs no out-of-the-box scanner catches, with low false-positive rate, scoped exactly to the patterns your team cares about. The cost is the engineering investment in authoring + maintaining the queries + integrating CodeQL into the development workflow.
For teams with mature AppSec functions, QL expertise on staff, and a long history of internal security patterns to encode, CodeQL is genuinely powerful. The org-specific queries become a security-engineering knowledge artifact that captures 'what does our team consider a security bug' in executable form. The GitHub-maintained query packs provide a baseline for canonical patterns; the team-authored queries extend coverage to org-specific surface.
Securie's specialists ship with their detection logic baked in. The trade is flexibility — Securie does not currently support custom-query authoring at the same level CodeQL does. For framework-aware coverage of well-known risk surfaces (Supabase RLS, broken auth, AI-features), the specialist depth is the right answer; the team gets the value without the QL investment. For org-specific business-logic coverage outside the specialist surface, Securie does not yet cover it.
The sandbox-verification commitment cuts across the comparison differently. CodeQL produces findings based on the QL query; the findings are precise per the query but not runtime-verified. Securie produces findings only when the sandbox successfully reproduces the exploit; the verification is empirical. For canonical bug classes where the QL query is well-tuned, the difference matters less. For framework-specific or logic vulnerabilities where the static analysis can be ambiguous, the sandbox verification is the load-bearing commitment.
The procurement-shape difference also matters. CodeQL standalone is OSS — the cost is engineering time. GitHub Advanced Security wraps CodeQL with managed scanning + dashboard + auto-fix-suggestions at a per-active-committer monthly rate. Securie's tiers are flat capped-envelope. For teams with engineering bandwidth and OSS-comfort, the CodeQL path is cheap on dollars and expensive on time; for teams without that bandwidth, Securie is more dollar-cost but operationally simpler.
The sensible procurement combination for teams with AppSec maturity is: CodeQL for org-specific business-logic queries + Securie for the framework-aware AI-app surface with sandbox verification. The two complement each other on flexibility-vs-out-of-box, with low overlap on the specific surfaces each is best at.
Pricing
Free ($0) · Indie ($12) · Solo Founder ($49) · Startup ($299). Capped-envelope monthly.
OSS CodeQL CLI free for open source repos; GitHub Advanced Security per-active-committer for private repos. // TODO: verify github.com/pricing.
Migration playbook
Step 1: Inventory your team's QL capacity
What: Honestly assess: does anyone on your team have QL authoring experience? Have you ever written a CodeQL query, or maintained one? Would your team prioritize authoring + maintaining org-specific queries against deadline pressure?
Why: CodeQL's value depends on QL expertise. Without it, you get the GitHub-maintained query packs only — which is real value but does not unlock CodeQL's full flexibility. The choice between CodeQL standalone and Securie is partly about whether the QL investment fits your team.
Gotchas: Common mistake: assuming someone on the team will pick up QL during the procurement window. QL is a real programming language with a learning curve; treat the QL investment as a real cost.
Step 2: If you have QL expertise: pick CodeQL or run both
What: If your team can author + maintain QL queries, install CodeQL CLI for OSS repos or enable GitHub Advanced Security for private repos. Author org-specific queries that encode your business logic. If your stack also has framework-aware risk surfaces (Supabase, Next.js auth, AI features), add Securie for that surface.
Why: CodeQL's flexibility is unique; teams with the expertise should use it. Securie complements rather than substitutes — different surfaces, different strengths.
Gotchas: Watch for query-rot — QL queries against an evolving codebase need maintenance. Schedule quarterly review of the team-authored queries.
Step 3: If no QL expertise: pick Securie alone
What: Install Securie's GitHub App. Use the Day-1 specialists for the AI-app risk surface. Skip CodeQL until QL expertise is on the team or until an org-specific bug class genuinely requires custom queries.
Why: Without QL expertise, CodeQL's value is limited to the GitHub-maintained query packs. For most AI-built-app teams, Securie's specialists cover more of the actual incident frequency.
Gotchas: If you find an org-specific bug class Securie does not cover, file the gap. Securie's launch surface is intentionally focused; vendor support during early access can prioritize specialist additions.
Step 4: If running both: configure clear ownership boundaries
What: CodeQL for org-specific business-logic queries (team-authored, maintained over time). Securie for the framework-aware AI-app surface (out-of-box specialists, sandbox verification). Configure precedence so org-specific findings flow to CodeQL; framework-aware findings flow to Securie.
Why: The two products' strengths sit on different points of the flexibility-vs-out-of-box axis. Explicit boundaries prevent finding-duplication noise.
Gotchas: Re-evaluate the boundary as your QL library grows. If team-authored queries start covering surfaces Securie's specialists already cover, deduplicate.
Step 5: Decide based on team shape + risk profile
What: After evaluation, weigh: QL maintenance cost (engineer-time per quarter), specialist coverage match (Day-1 specialists vs your top risks), procurement fit (per-active-committer GHAS vs flat-tier Securie). Pick whichever combination matches your team.
Why: Both tools have honest scope. The decision is matching tool shape to team shape.
Gotchas: Do not let vendor-pitch breadth override actual fit. CodeQL's flexibility is real but only valuable if your team uses it; Securie's specialist depth is real but limited to the launch surface.
When to pick CodeQL
Your team has QL authoring expertise (or budget to hire it), you want full control over the query surface to find org-specific bugs your business logic encodes, and you are comfortable owning the maintenance of those queries over time.
When to pick Securie
You want out-of-the-box specialists for the AI-app risk surface with sandbox verification, you do not have QL expertise on your team, and you want one tool covering scanning + auto-fix + attestation rather than just the scanning layer.
Bottom line
CodeQL wins on flexibility — write any query you want, run it across the code-as-data graph, get precise results. Securie wins on out-of-the-box value — Day-1 specialists for the AI-app risk surface with sandbox verification, no query authoring required. The honest answer depends on whether your team has QL expertise and time to author queries.
FAQ
Can I use Securie and CodeQL together?
Yes — they cover complementary surfaces. CodeQL for org-specific business-logic queries authored by your team; Securie for the framework-aware AI-app surface (Supabase RLS, broken auth, AI-features) with sandbox verification. Most teams find the overlap is narrow because the strengths of each tool sit on different points of the depth-vs-flexibility axis.
Does CodeQL cover Supabase RLS / Next.js middleware bugs?
The GitHub-maintained query packs cover canonical SQL injection, XSS, SSRF, path traversal patterns. Framework-specific queries (Supabase RLS USING-without-WITH-CHECK shapes, Next.js middleware matcher mismatches) require team-authored QL queries — they are not in the default packs as of 2026. For framework-aware coverage out of the box, Securie's specialist surface is the better fit.
Why not just use GitHub Advanced Security (which wraps CodeQL)?
GitHub Advanced Security is a real choice — see [Securie vs GitHub Advanced Security](/compare/securie-vs-github-advanced-security) for that head-to-head. The Securie-vs-CodeQL framing is for teams considering CodeQL standalone (open-source CLI, self-managed) vs Securie. The comparison shape differs from GHAS — CodeQL standalone has no managed runtime, no fix-generation, and requires self-hosted scanning infrastructure.
What about query authoring for our org's specific business logic?
If your business logic encodes specific authz patterns, internal API conventions, or framework-specific bugs that no out-of-the-box tool catches, CodeQL's flexibility is the right answer — your team authors the queries and runs them. Securie's specialists do not currently support custom-query authoring at the same level. For org-specific business-logic coverage, run CodeQL alongside Securie.
How does the procurement model compare?
CodeQL standalone is open-source and free for open-source repos; the cost is engineering time to author + maintain queries + run scanning infrastructure. GitHub Advanced Security wraps CodeQL with managed scanning + dashboard + auto-fix-suggestions for ~$49/active committer/month. Securie sells capped-envelope monthly subscriptions ($0-$299 self-serve; Enterprise tier available). For solo founders, the OSS CodeQL path requires significant engineering investment; Securie's self-serve flow is operationally simpler.