Securie vs Snyk
Snyk pattern-matches code and produces findings. Securie proves each finding with a working exploit in a sandbox, writes the fix, and opens a pull-request comment you can merge in one tap. Full feature, pricing, and best-fit comparison.
Teams comparing Securie and Snyk in 2026 are almost always asking a specific question: 'we are on Next.js + Supabase + Vercel, Snyk is noisy, is Securie a real replacement or just another pattern matcher with a better homepage?' This page is an honest answer to that question. Snyk is the incumbent for good reasons — broad language support, mature SCA, container and IaC coverage, 12 years of enterprise deployment experience. Securie targets a narrower slice but goes materially deeper on it: the AI-generated code on AI-native frameworks where Snyk's pattern engine struggles.
The shape of the comparison is not 'which tool is better overall'. Snyk is better overall if you run six languages and need container + IaC + SCA + SAST in one contract, and you have the team to triage the noise. Securie is better for the specific profile of AI-built application teams — the shape of risk is different, the toolkit that addresses it is different, and the honest comparison lives at the framework-specific level rather than at the feature-checkbox level.
This page walks the tradeoff at the architectural level (pattern-match vs sandbox-verify), the practical level (what the tools catch and miss on specific bug classes), and the organizational level (what team shape each tool serves). If after reading you conclude Snyk is still the right fit, we have tried to be honest enough that you can trust the conclusion. If Securie is the right fit, the comparison is structured so the decision is defensible to your CTO and to your board.
Snyk is the incumbent and has broad language coverage. Its weakness is signal-to-noise — G2 places Snyk's false-positive score at 6.8 out of 10 and Snyk Code silently skips any file over 1 MB. Securie solves those two problems head-on with a sandbox-verified exploit requirement and a framework-aware auto-fix PR, at the cost of narrower language coverage at launch.
Feature comparison
| Securie | Snyk | |
|---|---|---|
| Finding verification | Every finding reproduced as a working exploit in a sandboxed fork of your app | Pattern-based; no runtime proof per finding |
| False positive rate | Zero by construction — no exploit, no ticket | ~70% in public G2 reviews; ~6.8/10 FP score |
| Auto-fix PR | Framework-aware patch as a one-tap pull-request comment | Snyk Fix PR available (Enterprise tier only); suggestion quality varies |
| Supabase RLS | Specialist agent, first-class | No specialist; SQL injection only |
| Broken access control | BOLA / BFLA / IDOR specialist with intent-graph reasoning | Generic authz rules |
| AI-feature security | Prompt injection, tool-scope abuse, RAG poisoning, jailbreak regression | None |
| Language scope | TypeScript + JavaScript at launch; Python + Go on Series-A roadmap | TypeScript, JavaScript, Python, Java, Go, Rust, Kotlin, Swift, C/C++, PHP, Ruby |
| File-size limits | No cap — full repo scanned | Silently skips files > 1 MB |
| Deploy models | SaaS (sealed enclave) or Customer-VPC (your own cloud) | SaaS only |
| Audit artefact | Signed in-toto + SLSA attestation per scan | Findings export, not attestation |
Where the difference shows up in practice
A Server Action with a permissive Zod schema accepts an attacker-controlled URL
Snyk: Snyk's pattern engine flags the `fetch()` call inside the Server Action as potential SSRF. The severity is marked High because the pattern is unverified. The developer triages, discovers the Zod schema limits the URL to 'https://our-cdn.example.com/*', and dismisses as false positive. Two hours of engineering time spent.
Securie: Securie's SSRF specialist reads the Zod schema, identifies that the regex is anchored permissively (does not escape the dot, allows subdomain wildcards), and generates a candidate attacker payload: 'https://our-cdn.example.com.attacker.controlled/'. The sandbox executes the Server Action with this payload, observes that the fetch() succeeds against the attacker-controlled domain, and ships the finding with the reproduced exploit + a corrected Zod schema as the patch. Forty seconds of scan time; zero developer triage because the finding came with proof.
A Supabase RLS policy that looks correct but does not filter on INSERT
Snyk: Snyk does not have a Supabase RLS specialist. The SQL CREATE POLICY statement is not parsed with Supabase semantics. The bug — a USING clause without a WITH CHECK clause on an INSERT-enabled policy — is invisible to the scanner. Developer ships, incident two months later when a user discovers they can insert rows on behalf of other users.
Securie: Securie's Supabase RLS specialist parses CREATE POLICY statements with full Supabase semantics. It flags: 'Policy \'orders_owner_rls\' allows INSERT but lacks a WITH CHECK clause — the row owner is not enforced on insert'. The sandbox reproduces the attack (inserts a row claiming ownership by a different auth.uid()) and verifies the policy permits it. Fix generated: add `WITH CHECK (auth.uid() = owner_id)` to the policy definition. Ships on the same PR that introduced the bug.
A leaked OpenAI API key in an AI-generated endpoint
Snyk: Snyk's secret scanner pattern-matches OpenAI keys in source code. If the key is committed in source, Snyk flags it. If the key is in an environment variable read by the runtime (the more common case), Snyk does not know whether the key is valid, active, or already rotated — it flags the pattern and asks the developer to check.
Securie: Securie's secret specialist detects the key pattern and live-validates it against the OpenAI API. If the key is valid and active, the finding carries 'Live key confirmed' and an auto-rotate PR is opened (mint a new key via the OpenAI management API, update the environment variable, revoke the old key). If the key is invalid/inactive, the finding is deprioritized because the exposure is mitigated. Developers see validated exposure, not pattern-match possibility.
A dependency CVE published 10 minutes ago on an npm package you use
Snyk: Snyk's vulnerability database updates on its own schedule — typically 4-24 hours after CVE publication. A CVE disclosed 10 minutes ago may not yet be in Snyk's database; the scan does not flag it. The team unknowingly ships a deploy that includes the vulnerable version.
Securie: Securie's CVE-to-block pipeline polls npm advisory feeds at 60-second intervals and syncs with NVD. New CVEs are propagated to the deploy-gate within 15 minutes of publication. A deploy that tries to ship with the vulnerable package is blocked at the Vercel Integration layer; the team sees a block reason ('npm:pkg-name < 1.2.4 vulnerable to CVE-2026-XXXXX, disclosed 14 minutes ago'). Upgrade PR is auto-opened.
The deeper tradeoff
The architectural pivot between Snyk and Securie is pattern matching vs exploit execution. Snyk's strength — and what has made it the category default for a decade — is a robust pattern engine with broad language coverage. For a large class of bugs in classical web applications (SQL injection in a Rails app, XSS in a Django view, path traversal in a Node backend), the pattern engine is sufficient. The bug shape matches a well-known pattern, the pattern engine detects it, and the developer fixes it.
AI-generated code breaks this model at the seam. The bugs in AI-generated Next.js code do not look like SQL injection in Rails. They look like: a Server Action that accepts FormData, validates it with a Zod schema too permissive to catch the actual attack, and passes the sanitized-looking output to a function that trusts its input; a Supabase RLS policy that includes `auth.uid()` but in the USING clause where it does not filter INSERT operations; a middleware matcher that appears to protect /api/admin/* but matches on a prefix that does not include the version segment. These are not patterns Snyk's rule engine missed; they are patterns that look correct at the static level and are wrong at the execution level.
Only executing the code can distinguish the correct version from the incorrect one. Securie's sandbox does exactly this — for every flagged code shape, the sandbox reproduces a working exploit against a shadow copy of your application. If the exploit succeeds, the finding ships. If the exploit fails (input is sanitized upstream, middleware catches it, the route is unreachable), the finding is silently dropped. The 6.8 false-positive score that dominates Snyk's G2 reviews becomes structurally irrelevant — the sandbox, not the pattern match, is the final judgment.
This architectural difference cascades into everything downstream. Auto-fix quality is higher because Securie has ground truth (the exploit to test against). Attestation artifacts are stronger because each finding carries a reproduced-exploit trace. Compliance evidence is stronger because the sandbox output is auditor-consumable. The tradeoff is language coverage — at launch Securie covers TypeScript + JavaScript on Next.js + Supabase + Vercel, which is a significant scope but narrower than Snyk's multi-language breadth.
For teams whose risk concentrates in the TypeScript/Next.js/Supabase slice, the narrower scope is the right scope and the sandbox architecture returns more value. For teams with polyglot production and mature security engineering, Snyk's breadth remains defensible and the Securie comparison is asymmetric. The honest answer is to map the tool's strengths to your actual stack and your actual incident profile, not to generic feature-list comparisons.
Pricing
Free during invite-only early access. No credit card. Founding-rate discount for life when paid tiers ship.
Free: 100 tests/mo. Team: $25/dev/mo. Enterprise: $52–98/dev/mo (list)
Migration playbook
Step 1: Run Snyk and Securie in parallel on every PR for two weeks
What: Install Securie's GitHub App alongside your existing Snyk installation. Do not dismiss Snyk findings during the window; do not deduplicate with Securie's findings. Let each tool run independently.
Why: The comparison you need is real-bug precision per tool, not feature lists. Two weeks of parallel operation gives you ground-truth data on your own codebase.
Gotchas: CI pipelines that fail-fast on any security check may block deploys during the dual window. Configure Securie as an informational check initially; promote to required only after the evaluation completes.
Step 2: Categorize every finding as real-bug or noise
What: For each finding from each tool, classify after engineering review: (a) real bug fixed, (b) theoretical but not exploitable in context (dismissed), (c) false positive (pattern match on safe code). Weekly totals per tool: real-bug-count / total-findings-count = real-bug precision.
Why: The ratio is the comparison. Snyk will almost always win on total-findings-count; Securie typically wins on real-bug precision. Both metrics matter; the ratio captures them together.
Gotchas: 'Theoretical but not exploitable' can be a cop-out for dismissing real bugs. Be strict: if the finding represents a structural vulnerability that could become exploitable under different input, it counts as real.
Step 3: Tally triage hours
What: Per week, log hours spent triaging each tool's findings. Include: reading the finding, understanding the context, determining exploitability, either fixing or dismissing. Convert to loaded cost at $150/hour.
Why: The hidden cost of pattern-based tooling is the triage tax. Securie's sandbox pre-filter typically drops weekly triage hours significantly. The dollar-delta is the real comparison.
Gotchas: Teams with a mature triage culture may have already invested in noise-reduction rules on top of Snyk. That reduces the apparent gap. Record whether your baseline includes that investment.
Step 4: Decide based on the numbers, not the vendor pitch
What: After two weeks, compare: real-bug precision per tool, weekly triage hours per tool, dollar cost per real bug caught. If Securie's numbers are materially better on your stack, schedule the Snyk cancellation to align with your next renewal. If Snyk's numbers are better or the delta is small, stay with Snyk.
Why: The decision should be data-driven and defensible. Vendor marketing is not a substitute for measurement on your actual codebase.
Gotchas: Multi-year Snyk contracts have cancellation windows. Check the contract before committing to a switch timeline; mid-term cancellation typically does not refund.
Step 5: Preserve the audit trail during transition
What: Export Snyk findings history as SARIF before canceling. Preserve for SOC 2 / compliance audit continuity. Document the evaluation methodology and the results so future auditors can see the decision was evidence-based.
Why: The Snyk history is part of your vulnerability-management program's record. Losing it mid-audit window creates control gaps.
Gotchas: Snyk's export includes dismissal reasons. If you carry those dismissals forward to Securie manually, the sandbox re-verifies each one — many 'dismissed in Snyk' findings turn out to be genuinely noise the sandbox also drops, but some turn out to be real bugs Snyk's dismissal logic missed.
When to pick Snyk
You already run five languages in production, you need container scanning plus IaC scanning plus SCA in one bill, and you have a dedicated security engineer to triage the noisy queue.
When to pick Securie
You ship AI-built apps on Next.js + Supabase + Vercel, you do not have a dedicated AppSec hire, and you want fixes in your PR — not findings in a dashboard.
Bottom line
Pick Snyk if you are a multi-language shop that needs enterprise-wide SCA across Python, Java, Go, Rust, and container images today. Pick Securie if you are shipping AI-built apps on Next.js + Supabase + Vercel and you are tired of triaging noisy findings.
FAQ
Does Securie replace Snyk today?
For the TypeScript + Next.js + Supabase slice, yes. For teams that also need Python, Go, Rust, and container scanning, run both in parallel during early access and deprecate Snyk for covered surfaces.
Will you support Python soon?
FastAPI and Python support is on the Series-A roadmap. Customers on early access get first-beta access when it ships.
What about SCA (dependency vulnerabilities)?
Launch coverage includes malicious-package detection and new-CVE blocking within 15 minutes of public disclosure for npm. Full cross-language SCA is roadmap.
How long does the sandbox verification take per PR?
Typical end-to-end time from webhook to PR comment is 30-90 seconds for repositories under 200 KLOC. Large repositories or complex exploit reproduction can extend to 2-4 minutes. The CI pipeline is not blocked — Securie runs asynchronously and posts results when ready.
What happens if my Snyk custom rules catch something Securie does not?
Export the relevant Snyk custom rule pattern and open a support ticket — we add specialist coverage for validated org-specific patterns during early access. If the rule is genuinely one-off and cannot be generalized, consider keeping Snyk Team tier for that narrow use case while Securie handles the rest.
How do findings map between the two tools when both run in parallel?
Securie produces framework-specific findings (Supabase RLS, Next.js BOLA, prompt injection) that Snyk typically does not produce. Snyk produces generic findings (XSS patterns, prototype pollution, container CVEs) that Securie may not cover at launch. Overlap is narrow; most teams find less than 15% of findings appear in both tools, and the sandbox verification on Securie's findings makes the overlap ones higher-confidence.