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Heeler vs Semgrep.
Agentic Development Security, built for the AI SDLC and modeled around the service — versus the developer-first static-analysis platform. On the code layer this is close; Heeler separates on runtime context and validated remediation.
Two strong platforms, two strategies.
Semgrep is the developer-first static-analysis platform — Code (SAST), Supply Chain (SCA + reachability), Secrets, and Guardian for AI-generated code. Its signature strength is a transparent, user-writable rule engine and a large public Registry: detection you own.
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 three 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 close — Semgrep is excellent, and its rule engine is best-in-class. Heeler separates on two axes Semgrep lacks natively: runtime context (APIs, authentication, internet exposure) driving reachability-based prioritization, and remediation that makes the code change and proves it in CI.
Own your detection rules, or reason about runtime and prove the fix.
Semgrep is a developer-first static-analysis platform with a best-in-class rule engine and a strong Code/Supply Chain/Secrets/Guardian suite — but no native application cloud/runtime context. Heeler is a context engine with a real runtime layer and validated remediation as the payoff. Scope here is code security.
Developer-first static analysis, rules you own
Detect precisely; put the rule engine in developers' hands.
- →Semgrep Code (SAST, cross-function default + optional cross-file), Supply Chain (SCA + reachability), Secrets (semantic + validated), Guardian for AI-generated code
- →A transparent, user-writable pattern + taint DSL, interactive editor, and a large public rule Registry — the signature strength
- →No native application cloud/runtime, API, authentication, or internet-exposure model — runtime only via Wiz (push) or Sysdig (ingest) integrations
- →Remediation: AI Autofix draft PRs (beta), Rule-defined fixes, Multimodal suggestions; Upgrade guidance for JS/Python
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 three read-only connections, sensor-less
- →A real runtime layer: services fingerprinted to the exact commit, API endpoints + schemas enumerated, per-endpoint authentication modeled, internet exposure detected
- →Embeds in coding agents via MCP + skills to steer generation, and vets the skill/instruction files they load
- →Remediation makes the change and proves it: edits first-party code, builds/tests in your CI, repairs until green, opens a merge-ready PR (human review, no auto-merge) — SCA and SAST — closing only on a runtime-verified fix
Side-by-side, with a verdict per row.
Four states. Heeler-leaning where Heeler advances; explicit where Semgrep leads; honest about parity.
Heeler advantage
Heeler delivers a capability Semgrep 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.
Semgrep advantage
Explicit signal that Semgrep leads on this row.
Scorecard — 17 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ○ Semgrep advantage |
|---|---|---|---|---|
| Prevent | 0 | 3 | 1 | 1 |
| Fix | 1 | 2 | 2 | 0 |
| Operate | 2 | 3 | 2 | 0 |
| Total | 3 | 8 | 5 | 1 |
| Capability | Heeler | Semgrep | 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. | Semgrep Guardian bundles an MCP server, hooks, and skills into one install, scans every file an agent generates with Code / Supply Chain / Secrets, and prompts the agent to regenerate until scans come back clean — across Claude Code, Cursor, Copilot, Codex, Kiro, and Windsurf. A genuine, strong peer capability at generation time. | ✓ Parity |
| Agent security & governance | A skill catalog vets every skill/instruction file (skills.md, CLAUDE.md, AGENTS.md, .cursorrules, mcp.json) for malicious content with a 0–100 safety score and a benign/suspicious/malicious verdict (static pass + isolated LLM judge), mapped to MITRE ATLAS + OWASP LLM Top 10, gate-able in the pipeline. | Guardian scans the code an agent writes and can act on the agent via hooks, but Semgrep does not statically vet the agent's own skill/instruction files (CLAUDE.md, .cursorrules, mcp.json, AGENTS.md) for malicious or poisoned content — the emerging agent supply-chain surface. It secures the output, not the instructions. | ◐ Heeler edge |
| Custom rule engine & rule registry | Ships a curated detection set and lets you tune true-positive rate; findings are explainable with the matching pattern shown. Detection logic is Heeler-authored rather than an open, user-writable rule language with a public community registry. | Semgrep's signature strength: a transparent, developer-writable pattern + taint DSL, an interactive editor/playground, and a large public rule Registry the community contributes to. If you want to own and version your detection logic, no one does this better. A genuine Semgrep advantage. | ○ Semgrep advantage |
| PR guardrails & policy enforcement | Block / Warn / Observe with plain-English rule authoring (no YAML or Rego); scoped by global, repo, service runtime context, and branch; diff-only on net-new violations so inherited debt isn't penalized; a full guardrail catalog — compromised/malicious dependency, minimum package-age, unpinned dependency and unpinned Action, secrets with live validation, license, SAST severity, and SLO guardrails that block PRs against overdue repos; multi-SCM native checks; and an in-PR Fix Now that triggers a validated (CI-repaired) remediation PR. | Unified Policies split detection from remediation — choose rules, then Monitor / Comment / Block per project — with Jira, Slack, and PR/MR comment actions. Mature and rule-centric, but scoping isn't runtime-context-aware, the block decision is keyed to rule/severity rather than a broad supply-chain/secrets/SLO catalog, 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 management that validates pinning and minimum-release-age controls are actually configured across ecosystems (npm, pnpm, Yarn, Bun, uv, pip, Deno) — enforced at the PR and continuously re-evaluated. | Malicious-dependency detection (MAL badge, critical severity, block-on-policy) across nine ecosystems, drawing on GitHub Advisories, OSV, and Semgrep's own security research with sub-hourly advisory ingestion. Strong advisory-based malware detection; the broader preventive guardrail set (typosquat, unpinned, cooldown-as-policy, posture validation of missing controls) is not a first-class program. | ◐ 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. | Upgrade guidance uses program analysis + AI to mark findings Safe to upgrade or Breaking changes and can open an upgrade PR with guidance in the description. Genuinely useful — but AI-assisted rather than a deterministic selection, and available for JavaScript and Python only, on GitHub/GitLab Cloud. | ◐ Heeler edge |
| End-to-end autofix with CI repair loop | The agent branches, applies the upgrade, makes the required first-party code changes, and validates through CI iteratively — repairing its own build/test failures until green, or abstaining to a human. Opens a merge-ready PR with proof it builds; human review by default (no auto-merge — a fit for SOC 2 / change-management controls). Same loop for SAST. | Semgrep Autofix (beta) uses AI to generate a proposed change and opens a draft PR/MR in 2–10 minutes; you review and merge. It does not build or test the change, and does not iteratively analyze and repair its own CI failures — a failing check surfaces for a human rather than being fixed. | ● Heeler advantage |
| SAST autofix (validated code fixes) | A deterministic, strategy-matched transform anchored to the exact source-to-sink flow; built and tested in your CI, repaired until green or handed to a human; confidence-scored, human review, no auto-merge. | Three distinct paths: Rule-defined fix (deterministic, author-supplied per rule, applied inline), AI Autofix (draft PR, beta), and Multimodal Suggested fix (prose, no diff). Rule-defined fixes are deterministic but only as good as each rule's author and are inline edits; the AI path is generated and re-scanned rather than a transform proven in CI. | ◐ Heeler edge |
| Fix confidence / effort scoring | Every SCA remediation graded Easy / Medium / Hard via breaking-change analysis; Auto-Fixable label; per-transform confidence; a Remediation Workbench shows environment-wide distribution. | Safe-to-upgrade / Breaking-changes badges, reachability, EPSS, and confidence thresholds on AI suggestions give a comparable read on how safe a fix is to apply. Oriented to upgrade safety and exploitability rather than an effort grade across the backlog. | ✓ 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 — with a commit-and-push fallback. | Rule-defined fixes render as click-to-apply suggestions in the PR/MR and IDE, and Multimodal adds step-by-step guidance in the comment. Comparable in-PR delivery on the lines that matter. | ✓ Parity |
| Operate · continuous evaluation and automated response | |||
| SAST & SCA detection | Cross-function, cross-file source-to-sink taint from Heeler's own SAST engine; build-emulation SCA (lockfile optional) across 14 ecosystems with full direct/transitive/first-party/bundled resolution. | Mature SAST with cross-function analysis by default and optional cross-file (interfile) taint, backed by a large rule set and an AI-powered detection mode for business-logic flaws (IDOR, broken authorization); SCA with genuine code-level reachability. 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. The cloud/runtime dimension fingerprints running services to the exact commit, enumerates application API endpoints and schemas, models per-endpoint authentication (method, protocol, provider, protected-or-not, auth chain, token issuer), detects internet-facing exposure, and maps deployments and service-to-service connections — sensor-less. Everything downstream (prioritization, SLOs, workflows) reads from it. | A strong code-security suite (Code, Supply Chain, Secrets) but no native context engine and no application cloud/runtime, API, authentication, or internet-exposure model of its own. Runtime context exists only by integration — pushing findings into Wiz's graph, or ingesting deployment status from Sysdig (both require that third-party platform and its agents). | ● 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. | Regex + semantic analysis (dataflow, constant propagation across files/functions) + entropy, plus proprietary validators that make a live API call locally to confirm a secret is active — tokens never leave your environment — and historical (git-history) scanning. Genuinely strong; comparable for code secrets. | ✓ Parity |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) recomputed continuously: ranks a finding by whether it's reachable, deployed, and internet-facing, weighted by business tier and exploit maturity. Because Heeler knows the runtime natively, a dormant finding and a live, internet-facing one on a Tier-1 service never get the same score. | Ranks on severity, genuine code-level SCA reachability, EPSS, and AI component tags (payments, auth, infrastructure) — a strong code-and-exploit signal. Deployment-status weighting is possible, but only by ingesting runtime from a Sysdig integration; Semgrep has no native runtime, so on its own it can't tell a live, internet-facing finding from a dormant one. | ● Heeler advantage |
| Finding lifecycle & refactor-resilience | Lifecycle Active → Fixed → Deployed (gone from every running deployment, runtime-verified); introduced-vs-surfaced; cross-scan matching survives file moves and refactors via stable dedup fingerprints. | Finding statuses, branch history, and deduplication with reachability and scan history; robust, but without a runtime-verified 'deployed' close or documented refactor-resilient fingerprinting. | ◐ Heeler edge |
| Workflows & response automation | Always-on event-driven workflows (New Finding, New CVE, Compromised Dependency, SLO Expiring) that 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. | Remediation policies drive PR/MR comments, blocking, Jira tickets, Slack/email notifications, and webhooks with per-project conditions — mature routing and ticketing. But automation notifies and tickets; it doesn't execute a validated fix in-loop, and closure is scan/triage-state based rather than runtime-verified. | ◐ Heeler edge |
| CI/CD & supply-chain posture | CI/CD modeled as a peer ecosystem: depth-10 transitive action graph, Actions resolved to pinned SHAs with their bundled npm deps scanned, and guardrails on unpinned actions, excessive token permissions, and dangerous triggers; plus posture validation of pinning and minimum-release-age controls; CycloneDX SBOM with github-action components. | Rules can catch GitHub Actions script-injection patterns, and Supply Chain produces an SBOM and malicious-dependency coverage — real building blocks. But there's no CI/CD security posture: no pinned-SHA enforcement, token-permission or dangerous-trigger analysis, or transitive Action dependency-graph. Supply-chain coverage is package-intelligence-shaped. | ◐ 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 Semgrep produces only by integration
Heeler natively enumerates application APIs + schemas, models per-endpoint authentication, detects internet-facing exposure, and maps deployments and service connections — sensor-less. Semgrep has no runtime layer of its own: it can push findings into Wiz's graph or ingest deployment status from Sysdig, but both require that third-party platform and its agents.
Prioritization grounded in what's actually reachable and exposed
Heeler Risk ranks by runtime reachability, deployment, and internet exposure, weighted by business tier and exploit maturity. Semgrep ranks on severity, code-level reachability, EPSS, and component tags — strong signals, but without native runtime it can't separate a live, internet-facing finding from a dormant one on evidence.
Remediation that makes the change and proves it
Heeler edits the required first-party code and repairs its own CI failures until green — SCA and SAST — then opens a merge-ready PR for human review (no auto-merge, a fit for SOC 2 change control). Semgrep Autofix generates an AI draft PR but doesn't build, test, or repair its own failures.
Deep guardrails + autonomous workflows
Plain-English Block/Warn/Observe guardrails (compromised deps, min-age, unpinned, secrets, license, SLO) with in-PR Fix Now, plus event-driven workflows that auto-trigger a validated remediation PR and close SLOs only on a runtime-verified fix — beyond rule-keyed comment/block/ticket.
Securing the agent's instructions, not just its output
Heeler vets the skill/instruction files an agent loads (CLAUDE.md, .cursorrules, mcp.json, AGENTS.md) for malicious content with a safety score and MITRE ATLAS mapping. Semgrep Guardian scans the code an agent writes and regenerates until clean — strong, but it doesn't inspect the instructions themselves.
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-skill catalog. Side-by-side with Semgrep, if useful.
