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Heeler vs GitLab Ultimate.
Agentic Development Security, built for the AI SDLC and modeled around the service — versus the security stack of an integrated DevSecOps platform. On the code layer this is a real contest; Heeler separates on runtime context, validated remediation, and AI-supply-chain security.
Two strong platforms, two strategies.
GitLab Ultimate folds a strong scanner suite — Advanced SAST (cross-file taint), secret detection with push protection, SBOM-based SCA, container/DAST/IaC — plus native policy enforcement into the DevSecOps platform your code already lives in. Its strength is in-platform enforcement, though most of it is Ultimate-tier with AI features behind a Duo add-on.
Heeler was built for the AI SDLC, modeled around the service. One context engine assembles six dimensions of context — including a real cloud/runtime layer — from a few read-only connections, sensor-less. Its strength is depth: prioritization by what's actually reachable and exposed, and remediation that edits the code and proves it green in your CI.
The short version: on the code layer this is a real contest — GitLab's Advanced SAST, secret detection, and native enforcement are strong. Heeler separates on axes GitLab lacks: runtime context (APIs, authentication, internet exposure) driving reachability-based prioritization, remediation that repairs its own CI until green, and security for the AI supply chain — without a tier-and-add-on maze.
Enforce scans in the platform, or reason about runtime and prove the fix.
GitLab folds a strong scanner suite and native policy enforcement into the platform your code lives in — but has no application cloud/runtime context, and gates most of it behind Ultimate plus a Duo add-on. Heeler is a context engine with a real runtime layer and validated remediation as the payoff. Scope here is code security.
Security inside the DevSecOps platform
Scan and enforce where the code already lives.
- →SAST + Advanced SAST (cross-file taint, GA), secret detection (push protection), SBOM-based SCA, container/DAST/IaC scanning, vulnerability management, dashboards
- →Security policies enforce scans and MR approvals natively in GitLab's own CI/CD — a genuine strength for an all-in GitLab shop
- →No application cloud/runtime, API, authentication, or internet-exposure model — 'exposure' is a manual project tag
- →Most of the stack is Ultimate-tier; AI remediation and triage require a GitLab Duo add-on (billed in GitLab Credits)
Context-engine native, service-modeled
Six dimensions of context, one model, a validated fix at the end.
- →One context engine assembles six dimensions — code, cloud/runtime, business, ownership, threat, agent — from a few read-only connections, sensor-less
- →A real runtime layer: API endpoints + schemas enumerated from source, per-endpoint authentication modeled, internet exposure computed from deployment topology
- →Embeds in coding agents via MCP + skills to steer generation, and vets the agent instruction/skill files they load
- →Remediation makes the change and proves it: edits first-party code, builds it in a sandbox, repairs its CI until green, opens a merge-ready PR (human review, no auto-merge) — SCA and SAST
Side-by-side, with a verdict per row.
Four states. Heeler-leaning where Heeler advances; explicit where GitLab leads; honest about parity.
Heeler advantage
Heeler delivers a capability GitLab does not, or in a fundamentally different way that changes outcomes.
Heeler edge
Both deliver the capability. Heeler's implementation is materially better on a verifiable dimension.
Parity
Both products deliver the capability comparably.
GitLab advantage
Explicit signal that GitLab leads on this row.
Scorecard — 17 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ○ GitLab advantage |
|---|---|---|---|---|
| Prevent | 1 | 3 | 0 | 1 |
| Fix | 1 | 3 | 1 | 0 |
| Operate | 2 | 3 | 2 | 0 |
| Total | 4 | 9 | 3 | 1 |
| Capability | Heeler | GitLab Ultimate | Verdict |
|---|---|---|---|
| Prevent · stop risk from entering the codebase | |||
| Security at AI code generation | An MCP server + auto-loaded Agent Skills inject org-specific context (vuln details, safe upgrade paths, package impact, runtime exposure, service policy) into the agent's reasoning at the moment of decision — steering secure generation before code exists, across Claude Code, Cursor, Copilot, Windsurf, Codex. | GitLab Duo brings AI into the dev loop — Code Suggestions, agentic chat, MCP client/server support, and @-mentionable external agents (Claude Code, Codex) — but it's productivity-first: it doesn't inject security context to steer secure generation, and Duo is a paid GitLab Credits add-on on top of the seat tier. | ◐ Heeler edge |
| Agent security & governance | A dedicated inventory scores every agent instruction/skill/config file (CLAUDE.md, AGENTS.md, GEMINI.md, .claude/skills, .cursor/agents, .mcp.json) 0–100 (below 70 = At Risk) across Static, LLM (intent), and External-reference risk, with a Malicious / Suspicious / Benign verdict — across Claude, Cursor, Gemini, Codex, and OpenCode — and a synchronous API to gate files in CI. | No equivalent. GitLab secures code and pipelines, but the AI agent supply chain — the instruction, skill, and MCP-config files your developers' coding agents load and execute — is not inventoried, scored, or vetted for malicious or poisoned content. | ● Heeler advantage |
| PR guardrails & policy enforcement | Block / Warn / Observe with plain-English rule authoring; scoped by global, repo, service runtime context (tier, application, internet-accessibility), and branch; a full catalog — dependency vulnerabilities & hygiene, SAST, secrets with live validation, and SLO guardrails; native status checks across GitHub, GitLab, Bitbucket, and Azure DevOps; and an in-PR Fix that commits a validated (CI-repaired) change to the developer's branch. | Security policies (scan execution, MR approval, pipeline execution) enforce scans and approval gates with severity/scan-result conditions and org-wide scoping — genuinely strong and native (see the platform-enforcement row). But authoring is YAML policy, scoping isn't runtime-context-aware, and remediation isn't a one-click validated fix — plus policies are Ultimate-tier. | ◐ Heeler edge |
| Platform-native pipeline & MR-approval enforcement | Enforces via native status checks on GitHub, GitLab, Bitbucket, and Azure DevOps, reporting results as the PR's green check or red X; blocking itself relies on each SCM's branch protection. Two things offset not owning the pipeline: guardrail Violations are tracked through to a 'merged unresolved' state, and always-on workflows operationalize the response — routing, ticketing, and auto-triggering a validated fix — whenever a control is bypassed. And because Heeler is SCM-agnostic, one security model spans every SCM at once — a migration, a team or business unit on a different SCM, or an M&A that adds one is covered without re-platforming. | Because GitLab is the SCM and the CI/CD engine, it enforces scans and merge-request approval gates inside its own pipelines and MRs — org-wide security policies, a separation-of-duties model, and an internal policy bot make controls native and hard to bypass. For an all-in GitLab shop, that in-pipeline integration is a real advantage — though it only protects code that lives in GitLab. | ○ GitLab advantage |
| Software supply-chain prevention (deps) | A layered, proactive program: block malicious/compromised on sight, typosquat detection, minimum package-age cooldown (blocks freshly published malware before advisories exist), unpinned-dependency and unpinned-Action detection, dependency-hygiene scoring, and posture validation of pinning and min-release-age controls across ecosystems (npm, pnpm, Yarn, Bun, uv, pip, Deno) — enforced at the PR and continuously re-evaluated. | SBOM-based and Continuous dependency scanning match components against the GitLab Advisory Database, with transitive coverage and dependency-path visualization (Ultimate). But malicious-package detection exists only as an experiment (Libbehave, runtime behavior), and there's no typosquat, unpinned, minimum-age-cooldown, or posture-validation program at the PR. | ◐ Heeler edge |
| Fix · solutions, not tickets | |||
| Deterministic upgrade selection | Computes the fix from the dependency graph + changelog + breaking-change detection + reachability of called methods: the lowest version that clears the CVEs and adds none; for transitive vulns, the smallest first-party ancestor bump that resolves the whole closure. 14 ecosystems. | 'Resolve with a merge request' opens a patch-based dependency-update MR — but only for Node.js/yarn (and container scanning), not a cross-ecosystem selection. For dependency bumps that break the build, Duo's agentic breaking-change resolution proposes fixes (AI, Duo add-on). Useful but narrow and largely AI-assisted rather than a deterministic selection. | ◐ Heeler edge |
| End-to-end autofix with CI repair loop | The agent branches, applies the change, makes the required first-party code edits, and validates in an isolated sandbox with a real build before the PR exists — then, once the PR is open, listens for CI results and repairs its own build/test failures with up to five follow-up commits, or hands off to a human. Merge-ready PR with proof it builds; human review by default, no auto-merge. SCA and SAST. | AI Vulnerability Resolution (Ultimate + a GitLab Duo Enterprise add-on) generates a merge request — or in-MR suggestion — that fixes a SAST finding for a fixed list of ~45 CWEs. It proposes a change; it does not build the change in a sandbox, and it does not iteratively analyze and repair its own CI failures — a failing pipeline surfaces for a human. | ● Heeler advantage |
| SAST autofix (validated code fixes) | A deterministic, strategy-matched transform (Parameterize / Escape / Allowlist / Path-Normalize) anchored to the exact source-to-sink flow, precomputed on every scan; built and tested in your CI, repaired until green or handed to a human; confidence- and effort-scored, human review, no auto-merge. | Agentic SAST Vulnerability Resolution auto-generates fix MRs for High/Critical findings using multi-shot LLM reasoning (Ultimate; AI/Duo). Genuinely useful, but it's model-generated and re-scanned across a bounded CWE set rather than a deterministic transform proven in your CI, and it depends on the Duo add-on. | ◐ Heeler edge |
| Fix confidence / effort scoring | Every fix is graded: SCA remediation Easy / Medium / Hard via breaking-change analysis, an Auto-Fixable label, per-transform confidence with the factors that lowered it, and an Effort rating; a Remediation Workbench shows environment-wide distribution. | GitLab surfaces severity, a dashboard risk score (severity + age + KEV + EPSS), CVSS and EPSS — strong signals for how urgent a vulnerability is, but oriented to prioritization, not a per-fix effort/confidence grade telling you how hard a given remediation will be to land. | ◐ Heeler edge |
| Inline PR fix suggestions | For guardrail violations, Heeler posts the fix as a native PR-review suggestion block — click-to-commit, on the exact offending lines — or commits it directly to the PR branch. | AI Vulnerability Resolution can deliver its fix as an in-MR suggestion (GA), and agentic breaking-change resolution proposes fixes inline on a failed dependency-bump MR. Comparable in-MR delivery (Ultimate; AI features need the Duo add-on). | ✓ Parity |
| Operate · continuous evaluation and automated response | |||
| SAST & SCA detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files from Heeler's own engine, with adaptive per-codebase rules; build-emulation SCA (lockfile optional) across 14 ecosystems with full direct/transitive/first-party/bundled resolution. | GitLab Advanced SAST (Ultimate) does cross-file, cross-function taint analysis with a source-to-sink code-flow view — genuinely strong, GA across nine languages — plus broad Semgrep-based coverage. SBOM-based SCA matches the GitLab Advisory Database with transitive coverage. Detection at the code layer is a genuine peer. | ✓ Parity |
| Six-dimension context engine (incl. cloud/runtime) | One graph across six dimensions — code, cloud/runtime, business, ownership, threat, and agent. It enumerates application API endpoints and schemas from source (REST, GraphQL, gRPC, SOAP, WebSocket, plus MCP tools and Spring Boot actuator routes), models per-endpoint authentication (method, CSRF/CORS, roles, issuer/provider), computes internet-accessibility from deployment topology + config, and maps services, deployments, and connections — sensor-less, read-only. | GitLab has a strong scanner suite but no unified context engine and no application runtime/API/authentication model. It has no endpoint inventory, no per-endpoint auth modeling, and no automatic internet-exposure detection — 'exposure' exists only as a manually assigned project security attribute used for dashboard filtering and policy scoping, not a detected signal. | ● Heeler advantage |
| Secrets detection | Full git-history scanning with per-finding commit attribution; live/active validation via per-provider API parsers, plus scheduled re-validation; rule- and entropy-based; runs offline in the CLI. | Secret detection (all tiers) is layered and strong: push protection blocks secrets at git push, pipeline detection scans branches, client-side detection catches secrets in issues/MRs, and some secret types are automatically revoked. Comparable — and push-protection at the git-push boundary is a genuine strength. | ✓ Parity |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) recomputed continuously: it ranks a finding by whether it's reachable, deployed, and internet-facing — internet-accessibility judged path-by-path, from a network entry point through the actual data flow — weighted by service tier and threat, and carries a per-finding SLO. A dormant finding and a live, internet-facing one on a Tier-1 service never score the same. | Ranks on severity, CVSS, EPSS, KEV, and age (the dashboard risk score combines these); static reachability exists but is 'Limited availability' and needs Advanced Vulnerability Management. With no runtime, deployment, or automatic internet-exposure model, ranking can't distinguish a live, exposed finding from a dormant one on evidence. | ● Heeler advantage |
| Finding lifecycle & refactor-resilience | Lifecycle Active → Fixed → Deployed — a finding closes only when Heeler confirms, from running deployments correlated back to source, that the vulnerable code is gone everywhere it ran; cross-scan matching survives file moves and refactors via stable dedup fingerprints. | Vulnerability states (Needs triage / Confirmed / Dismissed / Resolved) with Advanced Vulnerability Tracking that follows a finding as code moves between files — genuinely comparable on refactor-resilience. But closure is default-branch/scan-state based; there's no runtime-verified 'deployed and gone from production' close. | ◐ Heeler edge |
| Workflows & response automation | Always-on event-driven workflows (New Finding, New CVE, Compromised Dependency, SLO Expiring) route to the owning team with per-team Jira/Linear/Shortcut templates and Slack/Teams, auto-trigger a validated remediation PR, and close SLOs only on a runtime-verified fix — the full triage → route → remediate → verify loop with no human in the middle. | Security policies drive scans, approval gates, and a vulnerability-management policy that auto-resolves findings no longer detected on the default branch; notifications and ticket routing are supported. Mature and native, but automation enforces and notifies — it doesn't execute a validated fix in-loop, and closure is scan-state based rather than runtime-verified. | ◐ Heeler edge |
| CI/CD & supply-chain posture | CI/CD modeled as a first-class dependency ecosystem: the transitive action graph resolved several layers deep (composite + reusable workflows, plus bundled JS packages), impostor-commit / typosquat / abandoned-source integrity checks, and workflow guardrails on unpinned actions, excessive GITHUB_TOKEN permissions, dangerous triggers, OIDC misuse, and expression injection; plus posture validation and a CycloneDX SBOM including github-action components. | Pipeline execution policies and a CycloneDX SBOM give real CI/CD governance inside GitLab's own pipelines, and IaC scanning (KICS) covers infra files. But there's no CI/CD supply-chain security posture — no transitive action/workflow dependency graph, pinned-SHA enforcement, token-permission or dangerous-trigger analysis on the pipeline definitions themselves. | ◐ Heeler edge |
Where runtime context and validated remediation separate the two.
Every one of these maps back to the context engine — not features bolted onto a scanner.
Runtime context GitLab doesn't model
Heeler enumerates application APIs + schemas from source (including MCP tools and Spring Boot actuator routes), models per-endpoint authentication, computes internet-accessibility from deployment topology, and maps services, deployments, and connections — sensor-less. GitLab has no endpoint inventory, no per-endpoint auth model, and no detected internet-exposure; its 'exposure' is a manual project tag.
Prioritization grounded in what's actually reachable and exposed
Heeler Risk ranks by runtime reachability, deployment, and path-driven internet exposure, weighted by service tier and threat, with a per-finding SLO. GitLab ranks on severity, CVSS, EPSS, KEV, and age; its reachability signal is 'Limited availability' and there's no live-exposure or blast-radius input.
Remediation that makes the change and proves it
Heeler edits the required first-party code, builds it in an isolated sandbox before the PR exists, then repairs its own CI failures with up to five follow-up commits — SCA and SAST — opening a merge-ready PR for human review. GitLab's AI Vulnerability Resolution generates an MR for a bounded CWE set (Ultimate + a Duo Enterprise add-on) but never builds or repairs its own pipeline.
Security for the AI supply chain
Heeler inventories and scores the agent instruction/skill/MCP-config files your developers' coding agents load — 0–100, below 70 At Risk, with Static, LLM-intent, and External-reference risk and a Malicious/Suspicious/Benign verdict, across Claude, Cursor, Gemini, Codex, and OpenCode. GitLab has no equivalent for this emerging attack surface.
One capability set, not a tier-and-add-on maze
Heeler's runtime context, runtime-aware prioritization, plain-English guardrails, and validated CI-repaired remediation come as one product. In GitLab, most of the app-security stack (dependency scanning, DAST, dashboards, security policies, vulnerability management) requires Ultimate, and AI remediation and triage require a separate GitLab Duo add-on billed in GitLab Credits.
One security model across every SCM
Heeler applies the same guardrails, prioritization, and validated remediation across GitHub, GitLab, Bitbucket, and Azure DevOps at once, and its workflows capture any attempt to bypass a control. GitLab's native enforcement only protects code that lives in GitLab — so a migration, a team on a different SCM, or an M&A that brings in a new one leaves gaps until it's re-platformed.
See Heeler across your AI SDLC.
Heeler secures the whole AI SDLC — not just code. A demo runs it against your real repos and cloud: the runtime map, prioritization, validated remediation PRs, and the agent-file catalog. Side-by-side with GitLab, if useful.
