Checkmarx provides traditional SAST with manual workflows after deployment. Safeguard starts you clean with 10M+ zero CVE images and packages, then delivers AI-native supply chain security with Griffin AI's autonomous remediation across 100-level dependency depth. See why starting with zero CVE components and continuous self-healing beats periodic scanning.
AI-native supply chain security vs legacy SAST platform
3,000+ zero CVE images + 3,000+ Gold packages—malware-free from day one
None—traditional scan-and-fix after deployment
AI-native, cloud-native built from ground up for supply chain security
Legacy SAST platform—retrofitted for modern threats
Autonomous Auto-Fix with Griffin AI—no manual approval, fixes in minutes
Manual remediation workflows—generates reports requiring developer action
100-level dependency tracing—finds threats 40+ levels deeper
Limited dependency analysis—misses deeply nested supply chain threats
80% fewer with reachability analysis—only shows exploitable vulnerabilities
High false positive rate—requires significant manual triage
15 cloud providers, on-premises, air-gapped—true infrastructure flexibility
Limited cloud support—primarily SaaS with some self-hosted options
Complete SSCS: code, containers, AI models, CI/CD, SBOM, TPRM, Gold packages
SAST/SCA focused—limited container, SBOM, and third-party risk coverage
Continuous incremental scanning—real-time feedback without pipeline delays
Batch scanning (hours for large codebases)—blocks CI/CD pipelines
Complete lifecycle: generation, enrichment, validation, distribution, monitoring, attestation
Basic SCA component lists—no SBOM lifecycle management
FedRAMP HIGH, IL7, SOC 2 Type II ready—compliance-ready architecture designed for federal requirements
Limited federal compliance architecture—not designed for IL7 or FedRAMP HIGH
Griffin AI purpose-built for SSCS with autonomous OODA loop and self-healing
AI-augmented SAST—not purpose-built for autonomous supply chain security
Seven in-house, security-tuned models: five Griffin variants plus Eagle and Lion, each scoped to a different reasoning workload
AI Query Builder and AI-assisted features added to legacy SAST—no in-house multi-variant model lineup
Aegis attention architecture for long-context reasoning, with mixture-of-experts in the largest tier
No published in-house attention architecture
Models trained on a security-only corpus—no customer code, no general web crawl
No public commitment to a security-only, customer-code-free training corpus
Tokeniser extended for vulnerability classes, CVE IDs, package coordinates and exploit primitives
Standard tokenisation from upstream model providers
Every finding ships with HYPOTHESIS / CITED PATH / DISPROOF / PROPOSED PATCH—reviewable and machine-parseable
Findings returned with rule metadata and natural-language explanation—no contractual structured trace schema
Every finding is challenged by a disproof pass before it reaches the user
No published adversarial disproof step on AI-generated findings
Triage score routes each finding to the right model tier
No published auto-router across multiple in-house model tiers
Lion runs locally for inline IDE / pre-commit suggestions with sub-100ms p95 latency
IDE plugins call back to the platform—no local sub-100ms in-house model
Reasons across 12+ hops of cross-package taint, following data flow through transitive boundaries
Strong intra-application taint via SAST query language; cross-package supply-chain taint at the same depth is not the focus
Correlates related findings into a single reasoning pass so issue chains are explained together
Findings issued per query/rule; no published multi-finding correlation pass
Safeguard Code—a local AI coding agent for terminal and IDE workflows with full repo context
AI-assisted remediation surfaces inside the platform; no local terminal/IDE coding agent of equivalent scope
Safeguard MCP Server exposes tools to AI clients with capability scoping and sensitive-data egress guardrails
No published MCP server with capability-scoped tools and egress guardrails
Tracks the models, prompts and tools used inside your SDLC as a first-class AI-BOM artefact
Inventory is code/dependency-focused; no published AI-BOM tracking models, prompts and tool chains
Upstream patch + maintainer test-suite + draft advisory delivered as one coordinated disclosure package
Checkmarx Labs publishes research and disclosures—no bundled upstream patch + test suite + draft deliverable
Public threat intelligence feed available as RSS, JSON and STIX
Research blog and advisories are published; no equivalent multi-format public threat feed
Safeguard-published research with coordinated disclosure on real-world supply-chain incidents
Checkmarx Labs publishes regular supply-chain attack research—genuine strength of the vendor
Public bug bounty programme covering the Safeguard platform
Responsible disclosure process exists; no widely-public bounty programme of equivalent scope
Air-gapped and sovereign deployment with the full Griffin Zero (671B-MoE) and the rest of the lineup running in-region
On-prem and dedicated deployment is supported, but not with a full in-house large-model lineup
Three public constitutions (Security, AI, Human Values) govern model and platform behaviour
No published constitution-style governance documents of equivalent scope
Public product roadmap visible to customers and prospects
Roadmap shared under NDA in customer briefings—no fully public roadmap
Safeguard Academy—public training and certification programme on supply chain security
Codebashing provides secure-coding training—genuine strength of the vendor
Provenance bundle lets customers independently verify which model weights and pipeline produced a given finding
No published customer-verifiable model provenance bundle for AI findings
Three deployment shapes documented: shared cloud, dedicated, VPC-isolated, air-gapped, and sovereign
Cloud SaaS plus self-hosted options exist; air-gapped/sovereign with full AI lineup is not the focus
Audit logs exportable by the customer in JSON and CycloneDX
Audit logs available via API; no published CycloneDX-format export
Sandbox tenant for self-serve evaluation with realistic data and full feature surface
Trial access is sales-gated—no fully self-serve sandbox tenant of equivalent scope
Checkmarx retrofitted AI into legacy SAST. Griffin AI was architected from day one for autonomous supply chain security—not general-purpose AI adapted for security. Purpose-built OODA loop for continuous threat response.
Checkmarx focuses on SAST/SCA of source code. Safeguard protects the entire supply chain: dependencies 100 levels deep, containers in any registry, AI models, third-party vendors, and curated Gold packages.
Checkmarx generates security reports requiring manual developer fixing and approval workflows. Griffin AI autonomously fixes vulnerabilities and deploys remediations—no manual intervention, no delays, no backlogs.
Checkmarx reports all potential vulnerabilities without exploitation context—high false positive rate requiring manual triage. Safeguard uses reachability analysis—80% fewer false positives showing only exploitable threats.
Checkmarx legacy architecture has performance issues with large codebases. Safeguard cloud-native architecture provides continuous incremental scanning—real-time feedback without pipeline delays across 15 cloud providers.
Checkmarx provides component inventory lists. Safeguard Portal manages complete SBOM lifecycle: auto-generation, enrichment, validation, secure distribution, continuous monitoring, and EO 14028 attestation for federal compliance.