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HEAD-TO-HEAD COMPARISON
Heeler vs Apiiro.
Two Agentic Development Security platforms that prevent, fix, and operate on a deep code-and-runtime context engine — Apiiro is the closest competitor to Heeler in this set.
EXECUTIVE SUMMARY
Broad, real overlap — with divergence on fix, runtime, and closure.
The overlap is broad and real — deep code analysis, AI-SAST, SCA with reachability, validated secrets, API discovery, gen-time prevention, and risk-graph prioritization are genuine parity. Apiiro is the closest competitor to Heeler in this set.
Heeler's edges concentrate in the fix loop (fixes proven by a real sandbox build and CI repair until green, committed into the PR), native sensor-less runtime/cloud observation, GitHub-Actions-first-class SCA, agent-file malicious-intent governance, and runtime-verified closure.
Apiiro's genuine advantages are design-phase and material-change detection and broad third-party ASPM aggregation. Scope here is code security.
THE FUNDAMENTAL DIFFERENCE
Two deep-code engines; where they diverge is fix, runtime, and closure.
Both do deep code analysis, code-to-runtime, and agentic-development security, and both are recognized ASPM/ADS leaders. The divergence is narrower than with other tools: how a fix is proven (a real build + CI repair vs model validation), whether runtime/cloud context is observed or partly ingested from a CNAPP, and whether closure is verified in production. Scope here is code security.
APIIRO
Deep ASPM + agentic development security
Understand the code, guard the agent, prioritize the risk.
- →Native Deep Code Analysis (AI-SAST, SCA, secrets+validation, API discovery, code-to-runtime matching) + a Risk Graph for prioritization
- →Guardian Agent (Secure Prompt) steers AI coding agents in the IDE; AutoFix Agent delivers context-aware, model-validated fixes; design-phase and material-change detection
- →Ingests third-party SAST/SCA/secrets/container/cloud/pentest/bug-bounty findings — a genuine ASPM aggregation layer; lifecycle, SLA, and MTTR tracking
- →Cloud posture ingested from a CNAPP (e.g. Wiz); fixes delivered as IDE/PR changes validated against risk models rather than built and CI-repaired to green
HEELER
Modeled around the running service
Prevent, Fix, and Operate on one runtime-aware model.
- →One context engine across six dimensions (code, cloud/runtime, business, ownership, threat, agent), sensor-less and read-only — runtime exposure is a native input
- →Its own detection — SAST, build-graph SCA (14 ecosystems, GitHub Actions first-class), validated secrets, agent-file governance — plus an MCP server, Agent Skills, and a CLI
- →Prioritizes by runtime reachability and internet exposure; remediation is built in a sandbox and repaired in your CI until green, committed into the PR
- →Guardrails gate new risk at the PR; workflows operate to a runtime-verified close with per-finding SLOs
VERDICT FRAMEWORK
Side-by-side, with a verdict per row.
Four states. Parity-heavy and honest — Apiiro is the closest competitor in this set.
●
Heeler advantage
Heeler delivers a capability Apiiro doesn't, or in a 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.
○
Apiiro advantage
Explicit signal that Apiiro leads on this row.
Scorecard — 23 capabilities, scoped to code security
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ○ Apiiro advantage |
|---|---|---|---|---|
| Prevent | – | 2 | 2 | – |
| Fix | 1 | 2 | – | – |
| Operate | – | 5 | 9 | 2 |
| Total | 1 | 9 | 11 | 2 |
| Capability | Heeler | Apiiro | Verdict |
|---|---|---|---|
| Prevent · stop risk before and as it enters | |||
| Security at AI code generation | Heeler's MCP server hands the coding agent its runtime-aware context as it writes — 30+ read-only tools exposing live SAST, SCA, and secrets findings, endpoint exposure, runtime and deployment risk, guardrail status, and SLO pressure, plus named secure-development prompts — and can trigger Heeler's validated remediation with the right scope. Drop-in Agent Skills and the CLI cover the same surface locally, for Claude Code, Cursor, Codex, and VS Code. | Apiiro's Guardian Agent works from the IDE/CLI via its own MCP server and uses patent-pending Secure Prompt technology to rewrite a developer's prompt with the app's architecture, threats, and org policy — steering AI coding agents (Copilot, Cursor, Gemini) to generate secure, compliant code the first time, grounded in its Software and Risk Graph. A genuinely strong gen-time prevention layer; both companies are recognized in Forrester's Agentic Development Security landscape. | ✓Parity |
| CLI / local developer scanning | The heelercli runs local scans across the full surface — SAST, SCA, secrets (live credential validation), license, and malicious-package checks — with non-zero exit for pre-commit and CI, SARIF output, and policy-as-code; plus IDE reach through the MCP server. | Apiiro reaches developers locally through its Guardian and AutoFix agents in the IDE and CLI (VS Code, Cursor, Gemini Code Assist) over its MCP server — answering risk questions, guiding fixes, and applying context-aware changes in-editor, grounded in the same Deep Code Analysis the platform runs. A comparable local/IDE developer surface. | ✓Parity |
| PR guardrails & policy enforcement | Block / Warn / Observe guardrails post a native status check on GitHub, GitLab, Bitbucket, and Azure DevOps and gate PRs on new dependency vulnerabilities, dependency hygiene, SAST, secrets, and SLO breaches — scoped by runtime context (enforce only on Tier-1, production, or internet-facing services) — with a validated fix committed straight into the PR branch. Block relies on SCM branch protection to enforce. | Apiiro's Risk Graph policies comment on or block pull requests and break builds when a risky material change is introduced, and route changes that need review (threat modeling, pentest) to the right stakeholders. Strong risk-based PR gating; it comments/blocks and can deliver a one-click IDE fix rather than committing a validated fix into the PR branch, and gating keys on material-change and risk policy rather than a runtime-exposure scope. | ◐Heeler edge |
| Install-time package cooldown | Refuses too-new (and likely-malicious) dependency versions at install time — wired into the package manager on the developer's machine and in CI across npm, pnpm, Yarn, Bun, uv, pip, and Deno — backed by a PR-time minimum-age guardrail. | Offers a package cooldown policy (e.g. 72 hours on new versions) as part of OSS Package Reputation & Health, enforced through its platform and PR/CI flow to blunt newly published malicious packages. | ◐Heeler edge |
| Fix · resolve findings — code and dependencies | |||
| SAST autofix | Deterministic, strategy-matched transforms (Parameterize / Escape / Allowlist / Path-Normalize) anchored to the exact source-to-sink flow, precomputed per finding on every scan, confidence- and effort-scored, and delivered as PRs validated the same way as dependency fixes (sandbox build + CI repair). Findings needing an architectural change get written guidance instead. | Apiiro's AutoFix Agent generates context-aware fixes for SAST findings — tailored to your architecture, policies, and compensating controls — delivered as one-click changes in the IDE (via its MCP server) or as pull-request suggestions, and validated against contextual rules, risk models, and runtime exposure to avoid over- and under-correction. AI-generated and model-validated, rather than deterministic transforms proven by a build. | ◐Heeler edge |
| SCA autofix (upgrades) | Deterministic, multi-signal upgrade selection over the resolved graph — Heeler picks the version that clears the most risk with the least disruption (no new known vulnerabilities, proven adoption, cleared minimum-age cooldown), and for transitive vulns finds the smallest bump to the direct ancestor you control (npm, Go, Cargo). Every fix is built in a sandbox and repaired through your CI until green before merge. Auto-fixable across Java, Go, JavaScript, Python, and C#. | Apiiro's AutoFix Agent also remediates SCA findings with context-aware, runtime-exposure-validated changes surfaced in the IDE or as PR suggestions, tied to the owning developer. It fixes with AI + risk-model validation rather than deterministic multi-signal upgrade selection built and CI-repaired to green before merge. | ◐Heeler edge |
| Validated, merge-ready fixes (build + CI repair) | Heeler validates every fix twice: a real build in an isolated sandbox with your project's toolchain before the PR (mvn/Gradle, npm/pnpm/Yarn, pip/uv/Poetry, go build, dotnet), then — once the PR is open — it runs your CI and repairs its own build/test failures with up to five follow-up commits until green, or hands off with an explanation. Draft PR if the sandbox build didn't pass; human review, no auto-merge. | Apiiro validates a fix against contextual rules, risk models, and runtime exposure to prevent patches that break functionality or miss the root cause, and delivers it as a one-click IDE change or PR suggestion. That validation is model- and policy-based; the docs don't describe building the project in a sandbox and running your CI to repair the fix's own build/test failures until green. | ●Heeler advantage |
| Operate · detect, prioritize, and run the program | |||
| SAST detection | Path-aware, interprocedural source-to-sink taint analysis across functions and files — plus structural (Graphscan) and heuristic methods — from Heeler's own engine, with detection rules adapted per codebase, automatic reachability/exposure triage, and step-by-step data-flow proof. | Apiiro's native AI-SAST analyzes source with context from its software graph (code to runtime and business risk), validating true positives and filtering false positives — checking whether input is sanitized multiple layers deep in the call stack, filtering non-production and test code paths, and learning organizational sanitization patterns. A genuinely comparable context-aware static engine. | ✓Parity |
| Dependency vulnerability (CVE) detection | Build-graph SCA across 14 ecosystems that resolves the full direct-and-transitive graph itself from your manifests — and from committed lockfiles when present, without requiring one or changing your build — covering first-party and bundled dependencies too, matched with CVSS, EPSS, and known-exploited signals. GitHub Actions and reusable workflows are a first-class dependency ecosystem, scanned like any other. | Apiiro's native SCA analyzes dependencies to the leaf node — direct, sub-, and custom internal dependencies — flagging vulnerable and malicious OSS and risky licenses with software-graph and code-to-runtime context, and can also ingest third-party SCA findings. Comparable dependency coverage; it doesn't treat GitHub Actions and reusable CI workflows as a first-class scanned dependency ecosystem. | ◐Heeler edge |
| CI/CD & GitHub Actions supply chain | Treats CI/CD as a first-class supply chain: inventories every workflow and resolves referenced Actions and reusable workflows several layers deep, recording exact pinned commit SHAs. Scores action-level integrity — unpinned actions, impostor commits, typosquats, abandoned/archived sources, and compromised actions — plus token-permission, OIDC, and dangerous-trigger checks, and enforces an unpinned-action and minimum-age PR guardrail. | Software Supply Chain Security inventories CI/CD pipelines (including shadow pipelines) and enriches GitHub Actions with code context, flagging pipeline misconfigurations and weak branch protection, with guardrails at commit/PR/CI time; action SHA-pinning is recommended and enforceable as org policy. | ◐Heeler edge |
| Supply-chain hygiene beyond CVEs | Flags supply-chain risk beyond CVEs — malicious and compromised packages, OpenSSF Scorecard hygiene, unmaintained and typosquatted packages, and unpinned or downgraded dependencies — with per-package hygiene scoring. | Evaluates open-source risk beyond CVEs — malicious and unmaintained packages, typosquatting and dependency confusion, and OpenSSF Scorecard–based reputation and health (maintainers, activity, popularity). | ✓Parity |
| Secrets detection & validation | First-class secrets: full git-history scanning across all branches with commit attribution, active live validation (read-only calls to the issuing provider; an active secret is weighted as maximal impact), offline structural validation, and 700+ rules across 20+ languages; contained and gated at the PR. | Apiiro natively detects and validates exposed, hardcoded, and leaked secrets across code history, pipelines, AI tools, and documentation, validating them in production and correlating them in the Risk Graph for prioritization. Comparable first-class secrets detection with live validation. | ✓Parity |
| Dependency reachability & noise reduction | Heeler treats a dependency vuln as reachable only when two independent checks agree: runtime reachability (the library is loaded in a running deployment) and static call-graph reachability to the vulnerable functions across six ecosystems (Go, Java, Python, JS/TS, Rust, Ruby). Unreachable findings are auto-mitigated; elsewhere it uses runtime reachability and errs toward reachable. | Apiiro contextualizes open-source vulnerabilities by whether they're internet-exposed, used in code, and exploitable — using its call-flow analysis and code-to-runtime context to cut noise and focus on real-world risk. Comparable reachability-and-exposure noise reduction. | ✓Parity |
| Endpoint & API attack-surface discovery | Heeler enumerates every endpoint from source — REST, GraphQL, gRPC, SOAP, and WebSocket, plus the MCP tools/resources/prompts and Spring Boot actuator routes your code exposes — capturing each one's authentication posture and computing internet-accessibility from deployment topology, so your externally reachable, unauthenticated surface is inventoried with no runtime agents or traffic capture. | Apiiro discovers APIs from source through Deep Code Analysis and adds true runtime API endpoint matching — using machine learning to match live runtime traffic to the code APIs that serve it — inventorying the API surface with sensitive-data and exposure context. Comparable API discovery, with a strong runtime-matching approach. | ✓Parity |
| Cloud, runtime & business context | Heeler builds its own model of where code runs — containers, VMs, and serverless fingerprinted, each deployment mapped to the exact running changeset, internet-accessibility auto-detected, service tier (1–4) and prod/non-prod classified, service-to-service and identity edges resolved, and ownership resolved to a RACI — all read-only and sensor-less. This context is what makes exposure, prioritization, and routing real. | Apiiro's Code-to-Runtime matching passively traces running container images back to source (GCP/Azure/AWS, no tags/labels) and enriches findings with runtime context. For cloud posture it ingests from CNAPPs (e.g. Wiz) and production context and positions itself as complementary to CNAPP — so the deeper cloud/runtime picture is correlated from an ingested CNAPP rather than a deployment model Heeler builds and observes itself. | ◐Heeler edge |
| Runtime-aware prioritization | Heeler Risk (Urgent / Plan / Defer) ranks SAST and SCA findings by real exposure. For SCA, a vuln must clear a gate: reachable (runtime-loaded, plus a call-graph path where supported) AND internet-exposed (an active deployment on internet-reachable compute) AND not mitigated — fail any and it Defers, however high the CVSS. For SAST, exposure is path-driven from an internet-facing entry point, behind auth, touching sensitive data. Both are weighted by service tier and threat (KEV/EPSS; CWE class), with a per-finding SLO, re-scored continuously. | Apiiro's Risk Graph prioritizes with likelihood and impact from Deep Code Analysis, runtime context, and third-party threat databases — factoring application architecture, business nature, exploitability, whether code is applicative vs test, internet-facing, and in active development. A comparably sophisticated, context-rich prioritization engine. | ✓Parity |
| Design & material-change risk detection | Heeler gates newly-introduced risk at the pull request — comparing a branch against the default-branch baseline so only findings a change actually adds are flagged, and guardrails can block the merge. It doesn't analyze feature-request tickets before code is written, or track design/architecture material change as a distinct, compliance-oriented capability. | Apiiro detects risk at the design phase — a private LLM analyzes feature-request tickets before code is written — and continuously detects material code, design, and architecture change/drift across modules, repos, APIs, and AI assets, triggering review workflows, blocking PRs, or breaking builds only when a material change is introduced (used, for example, to satisfy SEC material-change disclosure). | ○Apiiro advantage |
| Multi-tool / third-party ASPM aggregation | Heeler is its own detection-and-fix engine. It brings native SAST, SCA, secrets, and agent-file analysis and exports findings (SARIF, API), but unifying, normalizing, and deduplicating the output of many other vendors' scanners is not its focus. | Alongside its native detection, Apiiro ingests, normalizes, enriches, contextualizes, and prioritizes findings from third-party SAST, SCA, secrets, container, and cloud tools plus pentests and bug-bounty programs — a genuine ASPM aggregation layer over the tools you already run, from a single pane of glass. | ○Apiiro advantage |
| Agent-file detection & governance | A dedicated repo-level inventory scores every agent instruction/skill/subagent/hook/MCP-config file (CLAUDE.md, AGENTS.md, .claude/skills, .cursor, .mcp.json, .codex) 0–100 across Static, LLM-intent, and External-reference risk, with a Malicious / Suspicious / Benign verdict, across Claude, Cursor, Gemini, Codex, and OpenCode — with a CI-gating API and a workflow trigger. | Apiiro discovers and inventories the AI supply chain in code — models, agents, MCP servers, datasets, skills, plugins, and extensions (an AI-BOM) — flags shadow AI, and governs and verifies the integrity and traceability of what AI agents touch. Broad AI-supply-chain visibility and governance; it inventories and governs those assets rather than risk-scoring each agent-instruction file 0–100 for malicious intent with a per-file verdict. | ◐Heeler edge |
| SBOM & dependency inventory | A live dependency inventory (with OpenSSF hygiene scoring) and CycloneDX SBOMs at five scopes — including runtime SBOMs per running service and per deployment, built from real runtime context — alongside global, application, and repository scopes. | Apiiro generates a live inventory across the software graph and multiple bill-of-materials types — SBOM, API-BOM, and AI-BOM — kept current with every code change through Deep Code Analysis. Comparable inventory breadth, with strong API- and AI-BOM coverage. | ✓Parity |
| Open-source license compliance | Enforces an allow/deny license policy — license-violation findings across direct and transitive dependencies, surfaced in inventory and gated at the PR with a license guardrail. | Custom license policies flag unapproved or high-risk licenses, scanning every pull request against a codified policy (failing builds on violations), with a dedicated OSS Licenses view and OSI/FSF risk detail. | ✓Parity |
| Automated response workflows & routing | A standing when-this-then-that engine across the whole estate: one trigger (new finding, fix available, compromised dependency, new secret, at-risk agent file, SLO expiring, PR merged with unresolved guardrail violations) → AND-combined conditions → up to three chained actions that share context. Actions span messaging (Slack/Teams/Google Chat/email), ticketing (Jira/Linear/GitHub/Shortcut, de-duplicated), and domain actions (Fix with Heeler Agent, Update SLO Override, Generate SBOM, Webhook), routed to the owning team by RACI, with a plain-English builder. | Apiiro's Risk Graph policies trigger workflows on risky material changes — opening a ticket with full context assigned to the right owner (via code-owner attribution), routing to threat modeling or pentest, commenting on or blocking PRs, and breaking builds — with lifecycle tracking (SLA, MTTR) across the program. Comparable risk-based workflow automation and routing. | ✓Parity |
| SLOs & runtime-verified closure | Every tracked finding under an SLO policy carries a per-band (Urgent / Plan / Defer) SLO clock; SLO-expiring events trigger workflows, and an Update SLO Override action can extend a due date automatically. Findings run Active → Fixed → Deployed, and closure is runtime-verified — a fix reaches Deployed only once every running deployment confirms the vulnerability is actually gone, not merely merged. | Apiiro tracks the full risk lifecycle from detection to remediation or acceptance, with SLA and MTTR tracking, evidence collection (AutoManage), and verification integrated at design, PR, and deployment stages. Strong lifecycle and SLA measurement; closure is tracked and evidence-backed rather than gated on every running deployment confirming the vulnerability is actually gone. | ◐Heeler edge |
WHERE HEELER ADVANCES
Where the two closest platforms diverge.
Broad parity, with a handful of places the difference is real.
01
Fixes proven by a real build and CI, not validated against a model
Both platforms auto-fix — and Apiiro's AutoFix is genuinely strong, validating changes against risk models and runtime exposure and delivering one-click IDE or PR fixes. Heeler goes one step further on proof: it builds every fix in an isolated sandbox with your toolchain and then, once the PR is open, runs your CI and repairs its own build/test failures with follow-up commits until green — or opens a draft PR with an explanation. A model-validated patch is not the same as one demonstrated to build and pass tests.
02
Runtime and cloud context Heeler observes, not correlated from a CNAPP
Apiiro's Code-to-Runtime matching is impressive — passively tracing running containers back to source — but for cloud posture it ingests a CNAPP (e.g. Wiz) and positions itself as complementary to one. Heeler builds its own deployment model sensor-less: each deployment mapped to the exact running changeset, internet-accessibility, service tier, and ownership — so exposure and prioritization run on context Heeler observes itself, with no CNAPP required underneath.
03
GitHub Actions and the CI supply chain as a first-class ecosystem
Heeler resolves the full dependency graph from manifests with no build and no committed lockfile, and treats GitHub Actions and reusable workflows as a first-class scanned dependency ecosystem — scoring pin status, provenance, and integrity (impostor commits, typosquats, abandoned sources). The CI/CD supply chain is a live attack surface that a code-and-dependency SCA alone doesn't cover.
04
Agent-file governance with a per-file malicious-intent verdict
Both platforms map the new AI supply chain — Apiiro's AI-BOM inventories models, agents, MCP servers, and skills broadly. Heeler adds a governance engine that risk-scores each agent-instruction file (CLAUDE.md, AGENTS.md, .mcp.json, skills) 0–100 across static, LLM-intent, and external-reference risk and returns a Malicious / Suspicious / Benign verdict, CI-gated — vetting the files that now steer your code, not just cataloguing them.
05
Runtime-verified closure, not closure by lifecycle tracking
Apiiro tracks the full risk lifecycle with SLA/MTTR and evidence, and verifies across design, PR, and deployment stages. Heeler moves each finding Active → Fixed → Deployed and closes it only when every running deployment confirms the vulnerability is actually gone — per-finding SLO clocks driving the program to proof-in-production, not a lifecycle state that says a fix was applied.
See Heeler on your codebase.
A demo runs Heeler against your real repos and cloud — native detection across the surface, the runtime map, the prioritization, the validated remediation PRs, and the agent-file catalog. Apiiro and Heeler overlap more than most; where it counts, we'll show what fixes proven in CI, natively-observed runtime context, and verified-in-production closure add on top.
