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Heeler vs SonarQube Security.
Heeler is Agentic Development Security built for the AI SDLC — one context engine spanning code, dependencies, runtime, and cloud; SonarQube is a code-quality suite with a security layer. This compares security capabilities only — code quality is SonarQube's ground, not Heeler's. Here's where Heeler advances across the security stack, where the two are equivalent, and what the AI SDLC needs beyond code scanning.
Built for the AI SDLC — one model, code to cloud.
Heeler runs SAST, SCA, secrets, agent-skill security, CI/CD analysis, supply-chain controls, PR guardrails, and runtime-aware risk on one context engine that reasons across code, dependencies, runtime, and cloud as one model, and remediates deterministically to a CI-green, merge-ready PR. SonarQube's ground is code quality with a security layer on top — a SAST engine plus SCA, secrets, and license checks — whose deeper dependency-security capabilities need the paid Advanced Security add-on, and which models no runtime, deployment, or business context.
A note on structure: some rows below — Advanced SAST, dependency SCA, malicious-package detection, SBOM, license policy — need the paid Advanced Security add-on; those are marked, the rest are base product.
Heeler reasons across code, dependencies, runtime, and cloud as one model. SonarQube measures code quality.
Heeler knows where each finding runs, whether it's reachable and internet-exposed, who owns it, and how to ship a fix that's proven green in your CI. SonarQube grades the code in the repository — quality gates, maintainability, and a security layer alongside — with no model of runtime, deployment, or business context. That gap is what shows up after a finding lands.
Code-quality first, security alongside
A quality engine with a security layer.
- →SAST built into the code-quality engine; Advanced SAST into open-source dependencies needs the Advanced Security add-on
- →SCA with CVE, EPSS, and KEV data — but transitive analysis needs committed lockfiles, or SonarQube must run your build tools
- →Advisory-based malicious-package detection; no behavioral analysis for novel or uncatalogued packages
- →Code-centric prioritization — no runtime, deployment, or business-context model
Context-engine native, AI SDLC-built
Six dimensions of context, every layer.
- →Multi-SCM (GitHub, GitLab, Bitbucket, Azure DevOps) + multi-cloud (AWS, GCP, Azure, Kubernetes, and Vercel)
- →SAST, SCA, secrets, agent skills, CI/CD, supply chain, CLI, PR guardrails, workflows — one engine
- →Context across code, cloud, business, ownership, threat, and agent dimensions
- →Prevent → Fix → Operate operating on one shared model
Side-by-side, with a verdict per row.
Five states. Heeler-leaning where Heeler advances; explicit when SonarQube leads; honest about parity.
Heeler advantage
Heeler delivers a capability SonarQube doesn't, 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.
SonarQube edge
Both deliver the capability. SonarQube's implementation leads on a verifiable dimension.
SonarQube advantage
Explicit signal that SonarQube leads on this row.
| Section | ● Heeler advantage | ◐ Heeler edge | ✓ Parity | ◑ SonarQube edge | ○ SonarQube advantage |
|---|---|---|---|---|---|
| Code security scanning | 1 | 1 | 2 | 0 | 0 |
| Remediation & the AI SDLC | 2 | 3 | 1 | 0 | 0 |
| Context, prioritization & governance | 6 | 1 | 1 | 0 | 0 |
| Total | 9 | 5 | 4 | 0 | 0 |
| Capability | Heeler | SonarQube | Verdict |
|---|---|---|---|
| Code security scanning | |||
| SAST | Native SAST on a proprietary AST + Symbol Property Graph with cross-function, cross-file taint analysis; findings correlated to runtime reachability and exposure. | A SAST engine with cross-file taint analysis, integrated with its code-quality gates. | ✓Parity |
| SCA / dependency scanning | Build-emulation dependency resolution with no lockfile required; full graph across direct, transitive, first-party, and bundled dependencies; reachability-aware. | SCA with CVE, EPSS, and KEV data; the deepest dependency capabilities need the paid Advanced Security add-on, and transitive analysis needs committed lockfiles or running your build. | ◐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. | SonarQube applies the project quality gate to new pull-request issues, including supported Infrastructure-as-Code issues, and can block merge through a required repository status check. | ✓Parity |
| 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 |
| Remediation & the AI SDLC | |||
| Secrets detection | Real-time, language-aware detection with active validation of whether a secret is live, across code and full git history; prevention at AI-code-generation time. | Detects hardcoded credentials and common secret patterns across code and history as part of its analysis. | ◐Heeler edge |
| Malicious-package detection | Behavioral SAST on package code catches novel, uncatalogued malware — C2 calls, postinstall hooks, anti-forensics — before an advisory exists; minimum-age and compromised-dependency guardrails. | Advisory-based malicious-package detection; flags packages once they appear in threat feeds. | ◐Heeler edge |
| IaC misconfiguration scanning | 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. | SonarQube analyzes Terraform, CloudFormation, Kubernetes, and Dockerfiles and manages the resulting issues through its standard project workflow. | ✓Parity |
| Automated remediation | Deterministic agentic remediation for both SCA and SAST: Heeler picks the change, applies it, validates in your CI until green, and opens a merge-ready PR. | AI CodeFix suggests a per-issue fix for a developer to review and apply; no automated dependency-upgrade PRs and no CI validation loop. | ●Heeler advantage |
| AI SDLC & agent-skill security | Secures the agent layer itself — inventories and risk-scores agent skills, MCP configs, and CLAUDE.md / AGENTS.md; security skills auto-load into coding agents at generation time. | Analyzes AI-generated code with its SAST engine and gives IDE-level feedback; doesn't secure the agent or skill layer itself. | ◐Heeler edge |
| 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 |
| Context, prioritization & governance | |||
| Risk prioritization | Ranks by reachability, runtime exposure, and business criticality, recomputed continuously and sensor-lessly across code, cloud, and threat context. | Prioritizes by CVSS + KEV + EPSS — exploitability signals, but code-centric with no runtime, deployment, or business-context model. | ●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. | Not offered. SonarQube is code-centric with no runtime, deployment, or internet-exposure model — no cloud fingerprinting or service mapping. | ● Heeler advantage |
| 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. | Not offered. SonarQube builds no API or endpoint inventory and models no per-endpoint authentication. | ● Heeler advantage |
| SBOM | Continuous SBOM across the full resolved dependency graph, exportable and queryable environment-wide. | SBOM export available via the paid Advanced Security add-on. | ◐Heeler edge |
| License compliance | License policy enforced from one model across every stage; flags disallowed licenses at the PR and in workflows. | License and dependency policy checks available via the paid Advanced Security add-on. | ✓Parity |
| 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 |
| 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 Heeler goes beyond a code-quality suite.
Security capabilities SonarQube either can't match or delivers on a different model.
Deterministic agentic remediation
Heeler makes the actual change — the dependency upgrade plus required code edits (SCA), or a deterministic before/after transform (SAST) — runs it through your CI until green, and opens a merge-ready PR. SonarQube's AI CodeFix suggests a per-issue fix for a developer to apply, with no CI validation loop.
Runtime-aware prioritization
Heeler ranks by reachability, runtime exposure, and business criticality, recomputed continuously and sensor-lessly. SonarQube prioritizes by CVSS + KEV + EPSS — exploitability scoring, but code-centric with no runtime, deployment, or business-context model.
Behavioral malicious-package detection
Native behavioral SAST catches novel, uncatalogued malware by technique — C2 calls, postinstall hooks, anti-forensics — before an advisory exists. SonarQube's malicious-package detection is advisory-based only.
No-lockfile dependency resolution
Build-emulation produces a full graph across direct, transitive, first-party, and bundled dependencies with no lockfile required. SonarQube needs committed lockfiles for transitive analysis, or must run your build tools in a secure environment.
Secrets with active validation
Real-time, language-aware detection with active validation of whether a secret is live — across code and git history — plus prevention at AI-code-generation time. SonarQube covers core hardcoded-credential detection.
Agent skills security
Inventory and risk-score every agent skill, MCP config, and CLAUDE.md / AGENTS.md file. SonarQube scans AI-generated code with its SAST engine but doesn't secure the agent or skill layer itself.
See Heeler across your AI SDLC.
Heeler secures the whole AI SDLC — not just code. A demo runs it against your real repos, dependencies, and cloud, then walks through how prioritization, remediation, and workflows come together. Side-by-side with SonarQube, if useful.
