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Dethernety vs KAVACH

Dethernety

dether.net

KAVACH

agnile.com

Side by side

FigureDethernetyKAVACH
Price fromFree—
Free planYes—
Free trialYes—
Open sourceYes—
WebYes—
Windows—Yes
APIYes—
Self-hostedYes—
Plans published54
Spec score · Threat Modeling Software8.7 (#1)7.7 (#8)
Data handlingThe maker says models, data, and findings stay on the user's infrastructure; optional cloud evaluations send only closed-vocabulary configuration attributes and discard the payloads.The maker says architecture inputs, TARA records, cybersecurity work products, and operational metadata are designed to stay within the customer-defined boundary.
IntegrationsIssue modules can connect with external trackers and sync remediation issues bidirectionally.The maker names DOORS, Polarion, Jira, and AUTOSAR ARXML as toolchain context or integration targets, where applicable.
PurposeDethernety is a graph-native threat modeling platform for building and analyzing architecture models.KAVACH is an automotive cybersecurity engineering workspace supporting ISO/SAE 21434 lifecycle workflows from vehicle architecture to traceable evidence.
SupportThe Enterprise edition includes dedicated support and deployment help.Program Deployment includes onboarding, training, governance setup, and Agnile engineering support across the programme.

Plans

Dethernety

  • Open SourceFree
  • BYODtPriced on request
  • BYODt Team — 5 or 20 seatsPriced on request
  • EnterprisePriced on request
  • Hosted SaaSPriced on request

Dethernety pricing →

KAVACH

  • Engineering Services AcceleratorPriced on request
  • EvaluationPriced on request
  • Program DeploymentPriced on request
  • Scoped PilotPriced on request

KAVACH pricing →

In their own words

Dethernety

Dethernety is an open-source threat modeling platform that represents architecture as a graph for building and analyzing system models. Teams can create and explore models in a browser, from code, or from reports. Analysis follows the architecture graph to examine attack paths, blast radius, and residual risk. Exposures map to MITRE ATT&CK techniques and controls to D3FEND countermeasures for coverage-gap analysis. The platform has JavaScript and TypeScript modules for design, analysis, and issues, plus an OPA/Rego policy engine. Issue modules can connect to external trackers and synchronize remediation work in both directions. Users host their own graph database, with Neo4j and Memgraph listed as options. Analysis can use deterministic queries, single-agent AI acceleration, or multi-agent LangGraph workflows. The open-source edition runs on user-controlled infrastructure and can operate fully offline without cloud calls. The maker says models, data, and findings remain on that infrastructure, and that there is no background telemetry or usage-data transmission. The GUI is aimed at security teams; the CLI supports DevOps, CI/CD automation, and threat-model-as-code. Open Source is free; Enterprise is self-hosted with contract pricing and dedicated support.

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KAVACH

KAVACH is an automotive cybersecurity engineering workspace connecting vehicle architecture to traceable cybersecurity evidence across ISO/SAE 21434 workflows. Its TARA process links assets, damage and threat scenarios, attack paths, risk assessment and treatment, security goals, controls, and evidence. The workspace includes architecture context, attack-tree editing, a traceability hub, vulnerability monitoring, a cybersecurity case, an R155 compliance matrix, and report generation. SBOM and vulnerability signals can connect affected components with assets, threats, and treatment decisions. The current release focuses on ISO/SAE 21434 Clauses 5–15, UNECE R155/R156 evidence, AIS 189/AIS 190 readiness, and post-production monitoring. Deployment choices are a customer-controlled desktop workspace, on-premise deployment, or a customer-dedicated EU cloud VPC. Manual engineering, deterministic automation, and optional private AI are available. Generated work products are structured drafts requiring engineer review and customer approval. Pricing is scoped per programme.

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