Securie vs Aikido Security
Aikido is the all-in-one security platform marketed at startups: SAST, SCA, container, IaC, secrets, and DAST in one dashboard with LLM-assisted false-positive triage. Securie covers a narrower surface and proves every finding with a working sandbox exploit. This page is the honest comparison for teams choosing between them.
Teams comparing Securie and Aikido in 2026 are most often startups asking: 'we want one security tool, we cannot afford a security engineer, Aikido looks like the all-in-one and Securie looks deeper but narrower — which is right for us?' The honest answer depends entirely on what you ship. If your stack is polyglot — Python on the backend, Go for services, Rust for the data plane, containers in production, IaC in Terraform — Aikido's tool-category breadth is real value and the breadth/depth tradeoff favors them. If your stack is the AI-built-app standard — TypeScript + Next.js + Supabase + Vercel, with AI features in production — Securie's depth on that exact slice is more valuable than Aikido's breadth across categories you do not heavily use.
The second question that drives the comparison is the trust question: how much do you trust the tool's output? Aikido's LLM-auto-triage is a sophisticated noise filter, but it is a filter on top of pattern-match output — the underlying findings remain probabilistic. Securie's sandbox verification is a structurally different commitment: the finding ships only if a working exploit ran. The trust gap matters more for solo founders and small teams where every triage hour is a real cost; it matters less for teams with a dedicated AppSec function that can absorb the triage tax.
This page covers the tradeoff at the architectural level (LLM-triage vs sandbox-verify), the practical level (what each tool catches and misses on AI-app bug classes), and the procurement level (single-platform breadth vs focused-tool depth). If after reading you conclude Aikido is the right fit, the comparison is structured to make that decision defensible. If Securie is right for your slice, the same.
Aikido is the broader product — six tool categories in one dashboard with LLM-driven triage to mute pattern-match noise. Securie is the deeper product on a narrower surface — every finding is reproduced as a working exploit in a sandboxed copy of your app, so the noise is dropped at the source rather than triaged after the fact. Pick Aikido if you need one bill for SAST + SCA + container + IaC + secrets + DAST today. Pick Securie if you ship AI-built apps on Next.js + Supabase + Vercel and you want zero false positives by construction.
Feature comparison
| Securie | Aikido Security | |
|---|---|---|
| Finding verification | Every finding reproduced as a working exploit in a sandboxed fork of your app | Pattern-match + LLM-assisted triage (auto-mute), not exploit reproduction |
| False positive rate | Zero by construction — no exploit, no ticket | LLM auto-triage targets ~85% noise reduction (vendor claim) // TODO: verify against current public benchmark |
| Auto-fix PR | Framework-aware patch validated against the reproduced exploit (regression-tested in sandbox) | AI-generated fix suggestions; quality varies; no regression-against-exploit test |
| Supabase RLS | Day-1 production-validated specialist with full CREATE POLICY semantics | Generic SQL pattern coverage; no Supabase-specific RLS specialist |
| Broken access control | BOLA / BFLA / IDOR specialist with intent-graph reasoning + sandbox replay | Pattern-based authz checks; framework coverage varies |
| AI-feature security | Prompt injection, MCP-tool scope, RAG poisoning, multimodal-guard — sandbox-verified | AI-SPM module exists (Lakera-class scope); pattern-driven, not sandbox-verified // TODO: verify current AI module scope |
| Tool-category breadth | SAST + SCA + secret scanning + IaC + AI security + runtime — focused on AI-app surface | SAST + SCA + container + IaC + secrets + surface-monitoring + DAST — broader by category |
| Language scope | TypeScript + JavaScript at launch; Python + Go on Series-A roadmap | TypeScript, JavaScript, Python, Java, Go, Ruby, PHP, C#, more // TODO: verify current language matrix |
| Audit artefact | Signed in-toto + DSSE + Sigstore-rekor attestation per scan (Ed25519, KMS-backed in production) | Findings export, compliance reports; not cryptographically attested |
| Deploy models | SaaS (Firecracker-backed sandbox) or Customer-VPC + on-prem TEE (Enterprise/Sovereign) | SaaS only |
Where the difference shows up in practice
A Supabase RLS policy with USING clause but missing WITH CHECK on INSERT
Aikido Security: Aikido's SQL scanning recognizes CREATE POLICY statements but does not parse them with full Supabase RLS semantics. The bug — USING without WITH CHECK on a policy that permits INSERT — looks structurally complete to a generic SQL scanner. The LLM auto-triage may flag it as 'review' but does not have the framework knowledge to mark it exploitable. Developer ships the policy; incident months later when a user inserts rows claiming someone else's auth.uid().
Securie: Securie's Supabase RLS specialist parses the policy with full Supabase semantics and identifies the USING-without-WITH-CHECK shape on an INSERT-permitting policy as the canonical vulnerability. The sandbox seeds a Postgres instance with the policy and replays the attack — INSERT WHERE owner_id = some-other-uid succeeds. Finding ships with the reproduced exploit + the corrected policy (`WITH CHECK (auth.uid() = owner_id)`) as the patch.
A leaked OpenAI key in an environment variable on a Vercel deploy
Aikido Security: Aikido's secret scanner detects key patterns in source code. Keys that ship via environment variable (the standard Vercel/Next.js path) are visible only at runtime. Aikido's surface monitoring may detect the key being used externally; standard scan does not validate liveness.
Securie: Securie's secret specialist detects the key pattern wherever it appears and live-validates against the OpenAI API. If the key is active, the finding carries 'Live key confirmed' + an auto-rotate PR (mint a new key, update the Vercel environment variable via the platform API, revoke the old key). If the key is inactive, the finding deprioritizes. Validated exposure, not pattern-match possibility.
A Server Action vulnerable to BOLA — user_id taken from FormData and used unchecked
Aikido Security: Aikido's authz pattern-checks may flag the Server Action if the user_id field maps to a known authz pattern. The LLM triage reads the surrounding context, sees a `getUser()` call earlier in the function, and mutes the finding as 'likely not exploitable.' The mute is wrong — the getUser() result is not actually compared against the FormData user_id.
Securie: Securie's BOLA/BFLA specialist reads the Server Action and the auth context. The intent-graph identifies that getUser() returns a value that is not used in the authorization check — the FormData user_id is dispatched directly to the database. The sandbox seeds two users, signs in as user A, submits the Server Action with user B's ID in the FormData payload — the request succeeds. Finding ships with the reproduced cross-tenant access + a fixed Server Action that compares getUser().id against the FormData user_id.
A new npm CVE published 12 minutes ago on a package in your lockfile
Aikido Security: Aikido's vulnerability database refreshes on a schedule — typically multi-hour. A CVE 12 minutes old may not yet be in Aikido's feed; the deploy passes. The team unknowingly ships the vulnerable version.
Securie: Securie's CVE-to-block pipeline polls npm advisory feeds at 60-second intervals. Within 15 minutes of CVE publication, the deploy-gate at Vercel/Netlify/Cloudflare/Fly/Railway blocks any deploy that includes the vulnerable package. The block reason is surfaced ('npm:pkg-name < 1.2.4 vulnerable to CVE-202X-XXXXX, disclosed 14 minutes ago') and an upgrade PR opens automatically.
The deeper tradeoff
The architectural pivot between Aikido and Securie is not pattern-match vs sandbox — it is pattern-match-with-LLM-triage vs sandbox-verified-finding-only. Aikido's bet is that the LLM era enables a different shape of pattern-engine: keep the regex/AST-based detection broad, then apply an LLM judge to mute findings that look 'not exploitable in context.' This is materially better than 2020-era SAST tools that surfaced everything. Auto-triage typically reduces visible-finding count by 60-85% on a typical codebase, which is a real productivity gain. The remaining question is whether the muted findings include real bugs the LLM mis-judged — a question the user only finds out about after a breach.
Securie's bet is structurally different: rather than triage pattern-match output downstream, replay every candidate finding as a working exploit upstream. The sandbox runs the exploit against a copy of the application; if the exploit succeeds, the finding ships with the trace; if it fails, the finding is silently dropped. The reduction in visible-finding count comes from runtime ground-truth, not from LLM judgment. The auto-fix path inherits the same property: the patch is regression-tested against the reproduced exploit, so a fix only ships if it actually causes the exploit to fail.
For AI-generated code on AI-native frameworks, the structural-vs-probabilistic distinction matters more than for classical web applications. AI-generated code does not produce well-formed pattern-match shapes — it produces code that looks correct at the static level and is wrong at the execution level. A Server Action with a permissive Zod schema, a Supabase RLS policy missing WITH CHECK on INSERT, a middleware matcher that misses a path prefix — these are bugs the pattern engine cannot reliably classify. The LLM triage on top can sometimes catch them and sometimes not; the sandbox can verify them every time.
The tradeoff is breadth. Aikido covers SAST, SCA, container, IaC, secrets, DAST, and surface monitoring under one platform. Securie's launch covers TypeScript + JavaScript SAST + Day-1 specialist surface + supply-chain CVE-to-block within 15 minutes + secrets + AI-feature runtime guards — the AI-app subset of Aikido's matrix, plus features Aikido does not ship (Sigstore attestation, runtime-eBPF correlation back to PR findings, post-quantum attestation roadmap). For teams whose risk concentrates in the AI-app surface, the deeper-but-narrower trade is the right one. For teams with polyglot production and infrastructure-as-code at scale, Aikido's breadth is hard to beat.
The procurement reality also differs: Aikido sells a startup-friendly all-in-one annual contract; Securie sells a capped-envelope monthly subscription that scales from $0 (Free) to $299 (Startup) to Enterprise/Sovereign tiers with TEE deployment. The buyer profile is different — Aikido fits the 'we want one bill' procurement preference, Securie fits the 'we want the best per-finding tool for our specific stack' preference. Both are valid, and both are large markets.
Pricing
Free ($0, 1 repo, 20 scans/mo) · Indie ($12, 3 repos, 100 scans/mo) · Solo Founder ($49) · Startup ($299). Capped-envelope — soft cap throttles, never charges over.
Free tier with limits · Pro tier (per-developer pricing) · Scale tier (annual contract). // TODO: verify current pricing tiers + per-dev rate against aikido.dev/pricing
Migration playbook
Step 1: Run Aikido and Securie in parallel on every PR for two weeks
What: Install Securie's GitHub App alongside your Aikido installation. Do not deduplicate findings between them. Let each tool produce its full output independently.
Why: The comparison is real-bug precision per tool, not feature-checkbox parity. Two weeks of parallel operation produces ground-truth data on your own codebase rather than vendor benchmarks.
Gotchas: If your CI fails on any security finding, configure Securie as informational during the window. Promote to required only after evaluating finding quality on your repo.
Step 2: Categorize every finding as real-bug, theoretical, or false positive
What: For each finding from each tool, after engineering review classify into: (a) real bug requiring fix, (b) theoretical but not exploitable in current code paths, (c) false positive on safe code. Compute weekly real-bug-precision per tool.
Why: The Aikido auto-triage already filters most pattern-match noise — the remaining surfaced findings are the comparison set. The question is what each tool catches that the other misses, weighted by exploitability on your stack.
Gotchas: Auto-triaged-muted findings in Aikido should be reviewed at least once during the window — sample 10% of muted findings to verify the LLM judge agrees with the engineering judgment. Mismatches there are signal.
Step 3: Map finding overlap and gap
What: For each finding produced by each tool, mark whether it appeared in (a) only Securie, (b) only Aikido, (c) both. Tally per category. Identify the bug classes where one tool sees what the other misses.
Why: The overlap-and-gap tally is the consolidation decision data. If Securie catches AI-app bugs Aikido systematically misses, and Aikido catches container/IaC bugs Securie does not cover, the answer is 'both' or 'pick one based on which surface dominates your risk.'
Gotchas: Some apparent gaps are coverage scope, not detection failure. A container CVE Securie does not flag because it does not scan containers is not a Securie miss — it is a coverage gap. Distinguish miss-on-covered-surface from out-of-scope.
Step 4: Tally triage hours and dollar cost per real bug
What: Per week, log engineering hours spent triaging each tool's output. Convert to loaded cost at $150/hour. Divide by real bugs caught to compute dollar-cost-per-real-bug.
Why: The hidden tax of pattern-based tooling is triage time. The hidden value of sandbox verification is reducing it to near-zero. Both numbers matter; the ratio captures them together.
Gotchas: If your team has invested in custom Aikido triage rules, that engineering investment is not free — count its cost as part of the Aikido total when computing dollar-per-real-bug.
Step 5: Decide: consolidate, run-both, or stay
What: After two weeks, if Securie's coverage matches your risk surface and the precision delta is material, schedule the Aikido cancellation at next renewal. If Aikido's breadth is load-bearing for your operation, stay on Aikido and add Securie for AI-app-surface deep coverage. If your risk is purely AI-app surface, consolidate on Securie.
Why: The decision should be evidence-based and defensible to your CTO and board. Two weeks of parallel data is more valid than vendor pitches.
Gotchas: Aikido annual contracts have cancellation windows. Check before committing to a switch timeline; mid-term cancellation typically does not refund.
When to pick Aikido Security
You need one tool for SAST + SCA + container + IaC + secrets + DAST + surface monitoring under one bill, you have a polyglot codebase across 4+ languages, and you have the engineering bandwidth to triage the LLM-auto-triaged finding queue weekly.
When to pick Securie
You ship AI-built apps on Next.js + Supabase + Vercel, you do not have a security engineer, you cannot afford the trust cost of even a small false-positive rate, and you want every finding to come with a reproduced exploit you can show your CTO.
Bottom line
Aikido wins on tool-category breadth and is the right call for teams that need everything-under-one-bill at a startup-friendly price. Securie wins on per-finding precision for the AI-app slice — the bugs in AI-generated Server Actions, Supabase RLS policies, and Next.js middleware are reproduced as working exploits before they ship to your inbox. The honest answer depends on whether your bottleneck is tool-category coverage or finding precision.
FAQ
Does Securie replace Aikido today?
For the TypeScript + Next.js + Supabase + Vercel slice, yes — Securie's Day-1 specialists (Supabase RLS, leaked secrets, broken auth) cover the highest-risk surface for AI-built apps with sandbox verification Aikido does not have. For teams that also need container scanning, IaC scanning, and DAST under one bill, run both during early access and consolidate after evaluating real-bug precision on your codebase.
Aikido's auto-triage already mutes most false positives. How is sandbox verification different?
LLM auto-triage is a downstream filter on pattern-match output — it can mute a finding the model judges 'probably not exploitable in context.' That judgment is a probability, not a proof. Sandbox verification runs the candidate exploit against a copy of your app — if the exploit succeeds, the finding ships with the exploit trace; if it fails, the finding is dropped. The two approaches differ on the ground-truth question: triage muting depends on the LLM's read of context; sandbox verification depends on the actual runtime behavior.
What about container scanning and DAST? Does Securie cover those?
Container scanning and full DAST are not in Securie's launch scope — runtime-eBPF for customer-app containers is on the roadmap (alongside the MVP per CLAUDE.md), but it is not the same surface as a container vulnerability scan. If container CVE coverage is a hard requirement, run both tools or stay on Aikido until Securie's container scanning ships.
Will running both tools double the noise on every PR?
Briefly during the parallel-evaluation window, yes — that is the point. The two-week parallel run gives you ground-truth comparison on your actual codebase. After the window, consolidate based on real-bug precision per tool: most teams find Securie's findings on covered surfaces are higher signal-to-noise, and Aikido's are higher quantity. The decision should be data-driven on your own codebase, not vendor pitch.
Aikido is a single platform; Securie is one tool. Does that matter for procurement?
It depends on your procurement reality. If you have an existing single-vendor preference (one bill, one MSA, one support contact), Aikido's breadth fits that procurement shape. Securie's procurement is simpler at small scale (GitHub App install, no enterprise contract for Free/Indie/Solo Founder/Startup tiers) and matches the procurement reality of vibe-coder/solo-founder/seed-stage teams. At Series-A and above, where procurement consolidation does start to matter, Securie's enterprise tier ships as Customer-VPC + TEE.
Is the comparison different for AI-feature security specifically?
Yes. Aikido's AI-SPM module covers pattern-level checks for prompt-injection-shape and tool-misuse-shape signals. Securie's AI-feature stack (llm-safety with Llama Guard 4 ingress + egress filter, multimodal-guard, rag-guard, mcp-guard scope catalog, prompt-inj corpus with ≥0.90 CI gate) is built for the live runtime path — every customer-bound LLM call passes through. If your app exposes a chat interface or an MCP server, the relevant comparison is at runtime, not at static-pattern scan time.