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Heeler vs Snyk.
Agentic Development Security, built for the AI SDLC and modeled around the service — versus a scanner suite across code and open-source dependencies. This is the closest comparison in the set; Heeler separates on one context engine spanning code to cloud and a fix that proves itself green in your CI.
Built for the AI SDLC, not just breadth of scanning.
Heeler runs one context engine across code, dependencies, runtime, and cloud — assembling six dimensions of context from a few read-only connections, then ranking every SAST and SCA finding by what's reachable, deployed, and internet-facing and shipping remediation that proves itself green in your CI.
Snyk scans across code (semantic AI SAST) and open-source dependencies (SCA + reachability), with Agent Fix autofix and an agent-security line on its own vulnerability database.
Closest comparison in the set: Snyk leads on vulnerability intelligence; Heeler separates on native runtime context — per-endpoint exposure that also covers your SAST findings, no Kubernetes connector — and remediation that repairs its own CI until green.
One context engine code-to-cloud with a proven fix, not just breadth of scanning.
Heeler runs one context engine across code, dependencies, runtime, and cloud — ranking every finding by real reachability and exposure and proving each fix green in your CI. Snyk scans across code and open-source dependencies on its own vulnerability database, with an agent-security line — but its runtime context is integration-derived and asset-level, and premium controls gate at Ignite/Enterprise.
Code and open-source scanning suite
Scan, prioritize, and fix across code and open-source dependencies.
- →Snyk Code (semantic AI SAST, interfile taint) and Open Source (SCA + reachability)
- →Snyk Agent Fix (agentic, self-verifying autofix) plus Fix/Upgrade/Backlog PRs; its own vulnerability database and research team
- →Evo agent security: agent supply-chain vetting, behavior governance (preview), secure-at-inception generation, AI-SPM
- →Runtime context is integration-derived (Kubernetes Connector / cloud), asset-level; risk-based prioritization, custom rules, policies, and dashboards gate at Ignite / Enterprise
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 — feeding prioritization across SAST and SCA
- →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 across files, builds it in a sandbox, repairs its CI until green, opens a merge-ready PR (human review, no auto-merge)
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when Snyk leads; honest about parity.
Heeler advantage
Heeler delivers a capability Snyk 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.
Snyk edge
Both deliver the capability. Snyk's implementation leads on a verifiable dimension.
Snyk advantage
Explicit signal that Snyk leads on this row.
Scorecard — 27 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ Snyk edge | ○ Snyk advantage |
|---|---|---|---|---|---|
| Prevent | 1 | 3 | 2 | 0 | 0 |
| Fix | 2 | 2 | 2 | 0 | 0 |
| Operate | 6 | 6 | 2 | 1 | 0 |
| Total | 9 | 11 | 6 | 1 | 0 |
| Capability | Heeler | Snyk | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| 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. | Snyk Studio / Trusted Output Assurance secures what agents generate at creation: it steers agents with secure-at-inception directives (hooks, rules, skills), scans first-party code and OSS deps during agentic workflows, and gives agents Snyk context via a local MCP server. A comparable capability at generation time. | ✓ Parity |
| Agent security & governance | A dedicated inventory scores every agent instruction/skill/config file (CLAUDE.md, AGENTS.md, .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 — plus a synchronous API to gate files in CI. | Snyk's Agentic Development Security is comprehensive: Agent Supply Chain Security vets the MCP servers, skills, and tools agents use (risk indexes 0–1000, issue at ≥600) across Claude Code, Cursor, Codex, Gemini CLI and more; Agent Behavior Governance evaluates agent actions in-loop before they run (open preview); and AI-SPM inventories AI assets in code. A comparable peer, and broad. | ✓ Parity |
| 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. | PR Checks act as an automated reviewer — delta-based pass/fail on newly introduced Open Source and Code issues, blocking via the SCM's branch protection — and security/license Policies change severity or ignore by condition. But policies are Open Source-only and documented as an Enterprise-tier feature, scoping isn't runtime-context-aware, and there's no in-PR one-click validated remediation. | ◐ Heeler edge |
| 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. | Malicious-package detection — the Security Research team curates daily, labels CWE-506 Embedded Malicious Code (usually Critical), and covers typosquatting, dependency confusion, and hijacking across npm/PyPI/NuGet/RubyGems and more. But Snyk 'does not consider the provenance of a scanned package' and can't yet distinguish internal from external packages, and there's no minimum-age-cooldown, unpinned, or posture-validation program at the PR. | ◐ Heeler edge |
| IaC pull-request guardrails | Heeler evaluates the Infrastructure-as-Code findings introduced by a pull request instead of failing a change because of pre-existing repository debt. It returns a native source-control check controlled by program policy: Observe records the violations and the decision that would have been made, Warn reports them without preventing merge, and Block fails the check so repository rules can require it. The same policy can move from observation to enforcement without creating separate rules or CI logic. | Snyk can gate Infrastructure-as-Code through CI/CD and infrastructure workflows. Its implementation guidance places prevention primarily in CI or Terraform Cloud rather than a standard repository pull-request check. | ◐Heeler edge |
| Agent-file PR enforcement | Heeler inspects pull-request changes to agent instructions, skill files, hooks, subagents, and MCP configuration, including CLAUDE.md, AGENTS.md, and SKILL.md. It combines deterministic checks, LLM-based intent analysis, and analysis of external references to identify prompt injection, tool poisoning, unsafe command execution, secret access or exfiltration, permission bypass, and related agent-control risks. The evidence and verdict are returned through Observe, Warn, or Block before the changed artifact is trusted by coding agents. | No documented capability. | ●Heeler advantage |
| Fix · resolve findings — code and dependencies | |||
| 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. | Automatic Fix, Upgrade, and Backlog PRs are multi-SCM, with score/severity thresholds and a breakability guardrail (it won't open a PR if the upgrade introduces higher-severity vulns than it fixes). Update automation — but PR-automation-shaped rather than a deterministic selection of the minimal version that clears the CVEs without adding new ones. | ◐ Heeler edge |
| End-to-end autofix with CI repair loop | The agent branches, applies the change, makes the required first-party code edits — across multiple files — and validates in an isolated sandbox with a real build before the PR exists, then listens for CI results and repairs its own build/test failures with up to five follow-up commits, or hands off. Merge-ready PR with proof it builds; human review by default, no auto-merge. SCA and SAST. | Snyk Agent Fix (formerly DeepCode AI Fix) is an agentic autofix — LLM guided by 35,000+ expert fixes, and it re-scans each candidate with the SAST engine and retries on failure. But it fixes single-file issues only, surfaces inline in the IDE (not as a PR), and its verification is a static re-scan — it doesn't build or test the change, or repair a failing CI pipeline. | ● 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. | Snyk Agent Fix generates fixes with a proprietary intelligence DB + LLM and self-verifies by re-running the SAST engine (retrying if the fix fails or introduces a new issue). But it's model-generated and single-file, delivered as an IDE suggestion, and validated by re-scan rather than a deterministic transform proven with a real build in your CI. | ◐ 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. | Priority Score and Risk Score (0–1000) fold in fixability, exploit maturity, EPSS, reachability, and business criticality, and the Fix-PR breakability guardrail signals upgrade safety. Comparable read on what to fix and how safe an upgrade is, oriented to prioritization more than a per-fix effort grade. | ✓ Parity |
| 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. | Snyk delivers fixes inline in the IDE (Agent Fix 'Generate AI fix'), as Fix/Upgrade PRs on the repo, and as review notes in PR Checks. Comparable in-workflow delivery, though the code-fix path is IDE-inline rather than an in-PR suggestion block. | ✓ Parity |
| Policy-managed remediation memories | Heeler stores administrator-approved remediation guidance as memories in program policy and scopes each memory to an organization or repository. On later remediation runs, the Remediation Agent retrieves the applicable memories and applies the repository’s conventions and constraints when generating a fix. The guidance is centrally governed and reusable; it is not a one-off prompt, a triage-suppression rule, or generic model memory. | No documented capability. | ●Heeler advantage |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files, from Heeler's own engine, with rules adapted per codebase (Taint, Graphscan, and heuristic methods) and automatic triage that folds in reachability and runtime exposure before a finding ever surfaces. 20 languages. | Snyk Code is a semantic AI-based SAST with interfile (cross-file) taint analysis for all supported languages except Ruby and a source-to-sink data-flow view, real-time in the IDE. A comparable peer at the code-detection layer. | ✓ Parity |
| SCA & SBOM | Build-emulation SCA resolves direct and transitive dependencies from your manifests and lockfiles — using a committed lockfile when present, without requiring one, and without building your project — across 14 ecosystems, including GitHub Actions and reusable workflows as a first-class ecosystem. Reachability is established two ways (call graph and loaded-in-a-running-deployment). And it emits CycloneDX SBOMs at five scopes: a global SBOM across your entire environment, plus application, repository (multi-module merged and deduped), and runtime SBOMs per running service and deployment. | Snyk Open Source is an SCA — direct and transitive vulnerabilities, license compliance, reachability, and fix PRs. But with limited exceptions it must build your project to resolve the dependency tree, it does not treat GitHub Actions as a dependency ecosystem, and its SBOMs are per-project / per-target (CycloneDX or SPDX via CLI or API) — there is no org-wide global SBOM and no runtime SBOM of a running service. | ◐ Heeler edge |
| Unified context graph | One graph unifying six dimensions — code, cloud/runtime, business, ownership, threat, and agent — sensor-less, with everything downstream (prioritization, SLOs, workflows) reading from it. Cloud/runtime and endpoint discovery are broken out in the rows below. | Snyk assembles application context (AI-SPM asset inventory, Insights, an evidence graph), but there is no single native context engine that downstream prioritization, SLOs, and workflows all read from — its context is integration-derived and assembled per product rather than one unifying graph. | ● Heeler advantage |
| Cloud & runtime context | Fingerprints running services to the exact commit, detects internet-facing exposure, and maps deployments and service-to-service connections across AWS, GCP, Azure, Kubernetes, and Vercel — sensor-less. | Snyk's runtime signals are integration-derived: the Kubernetes Connector and cloud/container integrations match running images to Projects at the asset level — not a source-derived map of deployments, internet-facing exposure, and service-to-service connections. Snyk Cloud covers cloud/IaC posture, not application runtime context. | ◐ Heeler edge |
| Endpoint & API discovery | Enumerates application API endpoints and schemas from source, and models per-endpoint authentication — method, protocol, provider, protected-or-not, auth chain, and token issuer. | Snyk has no application API/endpoint enumeration and no per-endpoint authentication modeling — it does not inventory your routes, their protocols, or their auth from source. | ● Heeler advantage |
| Runtime-aware prioritization | The headline difference. Heeler Risk (Urgent / Plan / Defer) is recomputed continuously across SAST and SCA alike, ranking every 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, with a per-finding SLO. It's native and sensor-less: no Kubernetes connector, no CNAPP, no agent to deploy, and the same live-exposure model covers your first-party code. | Snyk ranks with call-graph Reachability (Java/JS/TS/Python; C# early access), EPSS, and exploit maturity, plus asset context — Deployed and Public-facing. But those runtime signals are integration-derived (they need the Kubernetes Connector / cloud), asset/image-level (port + K8s-ingress-path granularity), and the public-facing computation explicitly ignores firewalls, network policies, and security groups; the newer Risk Score is Early Access and excludes Snyk Code — so SAST findings get no live-exposure rank at all. There's no per-endpoint, path-driven exposure model. | ● Heeler advantage |
| Vulnerability & threat intelligence | Heeler scores risk from a broad intelligence set — CVE and security-advisory feeds, CVSS and EPSS, known-exploited (KEV) status, exploit maturity, active supply-chain-campaign and IOC signals, a compromised/malicious-dependency catalog, and OSSF Scorecard maintenance signals — all folded into runtime-aware Heeler Risk. | Snyk also maintains its own Snyk Vulnerability Database and a dedicated security research team that audits widely-used packages, conducts proprietary research, and discloses vulnerabilities (contributing CVEs), alongside NVD and GitHub monitoring and a package health score. Operating that first-party research database and disclosure program is Snyk's own. | ◑ Snyk edge |
| 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 — a first-class secrets product. | Snyk Code detects hardcoded secrets as part of a SAST scan, but its documentation notes this 'does not act as a standalone secrets scanning tool' — full secrets scanning is delivered through a third-party partnership rather than a native product with live validation and history. | ◐ Heeler edge |
| 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. | Issue tracking with consistent, asset-scoped ignores that persist across scans and branches (useful for triage continuity). But closure is scan-state based; there's no runtime-verified 'deployed and gone from production' close correlating findings back to what's actually running. | ◐ 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. | Fix/Upgrade/Backlog PRs, PR Checks, Jira integration, and policy actions cover a lot of the loop, and the auto-open/auto-close PR machinery is established. But automation is scan- and PR-centric; it doesn't run a validated fix through CI in-loop, and SLA/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 token permissions, dangerous triggers, and expression injection; plus posture validation and a CycloneDX SBOM including github-action components. | Snyk covers SCA, Container, and IaC (with custom IaC rules at Ignite+), giving broad pipeline-adjacent coverage. But it doesn't model the CI/CD supply chain itself — no transitive GitHub Actions/workflow dependency graph, pinned-SHA enforcement, token-permission or dangerous-trigger analysis on the pipeline definitions. | ◐ Heeler edge |
| Sensitive-data inventory | Heeler analyzes application source to identify the sensitive data types handled by the code and records them in the application catalog. Each data type is connected to the repositories, services, APIs, and owning teams that process it, together with business criticality and available deployment or exposure context. A reviewer can move from a data type to the applications and interfaces that handle it and evaluate that data-handling risk with the same ownership and runtime context used for other application-security findings. | No documented capability. | ●Heeler advantage |
| Infrastructure-as-Code findings | Heeler evaluates Infrastructure-as-Code against policy and creates a persistent finding for each violation. The finding retains the affected file or infrastructure resource and links it to the repository, application, owning team, business criticality, and any connected cloud or runtime object. The same finding is used for prioritization, lifecycle tracking, workflow routing, pull-request guardrails, and remediation instead of remaining a one-time CI scanner result. | Snyk Infrastructure as Code scans configuration and policy issues across supported formats and developer workflows. | ✓Parity |
| GitHub audit-log behavioral checks | Heeler ingests the GitHub organization audit log for each connected organization and evaluates defined patterns across the resulting source-control events. A match creates a native finding that retains the actor, action, affected repository or organization, timestamp, and matching event context. The finding enters Heeler’s ownership, prioritization, investigation, workflow, and lifecycle tracking rather than remaining a searchable or exported log event. | No documented capability. | ●Heeler advantage |
| OpenSSF Scorecard repository health | Heeler records the repository’s OpenSSF Scorecard result and each check outcome on the repository object in the catalog. The same repository view links the signal to applications, ownership, vulnerabilities, dependency health, provenance, deployment, and exposure context. Teams can inspect which Scorecard checks reduced the result and evaluate repository-maintenance risk in relation to the applications that use the repository instead of treating the score as an external badge. | No documented capability. | ●Heeler advantage |
Where native runtime context and validated remediation separate the two.
Every one of these maps back to the context engine — not features bolted onto a scanner.
Native endpoint & runtime context, not asset-level inference
Heeler enumerates application APIs + schemas from source (including MCP tools and Spring Boot actuator routes), models per-endpoint authentication, and computes internet-accessibility path-by-path from deployment topology — sensor-less. Snyk's Deployed/Public-facing signals are derived from the Kubernetes Connector and cloud integrations, are image/asset-level (port + K8s-ingress granularity), and explicitly ignore firewalls, network policies, and security groups. No endpoint enumeration, no per-endpoint auth.
Remediation that builds and proves itself in CI
Heeler edits first-party code across multiple files, 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 review. Snyk Agent Fix is an agentic autofix, but single-file, delivered inline in the IDE, and verified by re-scanning with the SAST engine — it doesn't build, test, or repair a failing pipeline.
Runtime-aware prioritization that includes your first-party code
Heeler ranks SAST and SCA findings on the same runtime-aware model — reachable, deployed, internet-facing, weighted by tier — sensor-less. Snyk's newer Risk Score is Early Access and covers Open Source but not Snyk Code, and its runtime signals require standing up the Kubernetes Connector; SAST prioritization stays on code + reachability without live exposure.
SCA that needs no build — and a global, runtime SBOM
Heeler resolves direct and transitive dependencies from your manifests and lockfiles with build emulation — using a committed lockfile when present, without requiring one, and no project build — across 14 ecosystems including GitHub Actions, and emits CycloneDX SBOMs at five scopes: a global SBOM across your whole environment, application, repository (multi-module merged and deduped), and runtime SBOMs per running service and deployment. Snyk's Open Source scan must build the project (with limited exceptions), doesn't treat GitHub Actions as a dependency ecosystem, and produces per-project SBOMs only — no global or runtime SBOM.
One capability set, not a four-tier climb
Heeler's runtime context, runtime-aware prioritization, plain-English guardrails, and validated CI-repaired remediation come as one product. In Snyk, risk-based prioritization, custom rules, policy management, reporting dashboards, and license compliance all require the Ignite tier ($1,260/yr per developer) or Enterprise — not Free or Team — and Enterprise adds zero-day risk prevention, unified AppSec control, and full SDLC automation on top.
Secrets and CI/CD supply chain the platform fully owns
Heeler runs first-class secrets detection — git-history, live validation, scheduled re-validation — and models the CI/CD supply chain as its own ecosystem (GitHub Actions graph, token-permission and dangerous-trigger checks, minimum-age gating). Snyk's real secrets scanning is a third-party partnership, and it has no CI/CD action-graph posture.
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 Snyk, if useful.
