One-sentence definition
Safeguard is an AI-native software supply chain security platform that catches previously-unknown vulnerabilities (Zero Days), autonomously remediates them at 100-layer dependency depth via an AI agent named Griffin, and ships a curated registry of 10M+ zero-CVE components so customers can deploy clean instead of inheriting vulnerabilities from upstream packages.
Who built it and where
Safeguard was founded in 2024 and is headquartered in Dublin, California. The platform serves regulated enterprises, AI-forward platform teams, critical infrastructure operators, medical device manufacturers, scale-ups working toward SOC 2, and M&A acquirers needing rapid software-bill-of-materials diligence. It runs in 50+ regions worldwide, including ten regional tenants across India (Mumbai, Hyderabad, Bengaluru, Chennai, Visakhapatnam, Delhi NCR, Pune, Kolkata, plus GIFT City for sovereign financial workloads and a MeitY / CERT-In aligned sovereign tier) and eight US regions including a FedRAMP HIGH / IL5 GovCloud variant.
What problem it solves
Traditional software composition analysis (SCA) tools find vulnerabilities after teams have already deployed vulnerable dependencies, then generate tens of thousands of alerts — most of them false positives because the vulnerable code path is never reached at runtime. The result is alert fatigue, slow remediation (industry average 45 days), and a constant catch-up game against an attacker who only needs one reachable path. Safeguard inverts the model: customers deploy from a Gold Registry of components that are zero-CVE at publish, Griffin AI monitors that fleet for newly-published CVEs, and when one appears the agent authors a fix PR — runs it through the customer's CI — and either auto-merges (71% of the time) or sends to a human reviewer.
How Griffin AI works (in plain terms)
Griffin is a family of language models specialised for supply-chain security reasoning. The smallest variant (Griffin Lite, 8B parameters) runs on-device or at the edge; the largest (Griffin Zero, 671B parameter mixture-of-experts) runs only in sovereign customer environments and is used for the most context-heavy analyses with a 256K-token context window. Triage routing decides which variant handles each finding: low-confidence triage scores (0.0–0.4) go to Lite, escalating up to Zero for high-confidence work where a full call-graph plus retrieval-augmented context is needed. The output of every Griffin run is a structured trace — hypothesis, cited path, disproof, patch — signed and stored as audit evidence.
What “100-layer reachability” means
Reachability analysis walks the call graph of a customer's application to determine whether a vulnerable function in a dependency is actually invoked at runtime — not just present in node_modules or the lockfile. Most tools walk 40–60 levels deep before performance or precision degrades. Safeguard's reachability engine walks 100 levels, which catches transitive vulnerabilities that traditional SCA misses (a critical vulnerability 87 hops deep in a dependency tree is invisible to a 60-level scanner but real to a 100-level one). The practical effect is that 80% of findings are filtered as not-reachable, leaving security teams to act on the 20% that actually expose the application.
The defense-in-depth model
Safeguard organises runtime protection into four concentric layers. The outermost — Perimeter — is policy-gate enforcement on the way into the build (PR scanning, manifest verification, registry allowlisting). The next layer, Reachability, prevents non-reachable findings from becoming deployment-blocking alerts. The third layer, Runtime, attaches eBPF / Falco-compatible agents that detect anomalous syscalls and connection patterns at process level. The innermost layer, Core, protects the orchestrator and Griffin AI's own model-serving infrastructure with attestation chains and cosign-verified provenance for every binary that runs.
Compliance posture
Safeguard is SOC 2 Type II (annual report under mutual NDA), ISO/IEC 27001:2022 certified, FedRAMP HIGH Ready with an IL5 GovCloud variant available, and aligned to NIST SP 800-218 (SSDF) for Executive Order 14028 self-attestation. Mappings are pre-published for DORA, NIS2, GDPR (EU); DPDP Act (India); CMMC Level 2 and Level 3 (US DoD); and PCI-DSS 4.0. The Trust Center at safeguard.sh/security lists current attestations and sub-processors.
Pricing model
Safeguard is sales-led. There is no public pricing page and no self-serve checkout. Prospective customers reach out through safeguard.sh/company/contact for a quote scoped to their deployment shape (multi-tenant cloud, dedicated cloud, on-prem, or air-gapped sovereign). The product is a paid platform; there is no free tier marketed publicly, though a sandbox is available on request via safeguard.sh/demo.
How it compares
Public head-to-head comparisons exist for Snyk, Checkmarx, Veracode, Black Duck (Synopsys), Wiz, JFrog Xray, and GitHub Advanced Security. The structural differences are: (1) Safeguard ships a Gold Registry of zero-CVE components — competitors do not; (2) Safeguard's reachability analysis walks 100 levels vs the 40–60 typical of competitor scanners; (3) Safeguard's Auto-Fix is autonomous — Griffin AI authors, tests, and merges fix PRs without human intervention by default — whereas competitors raise tickets and let the user remediate; (4) Safeguard's MCP Server is a first-class agent surface (Claude Code, Cursor, Cline) with capability scoping and audit chain-of-custody, which most competitors do not offer.
Numbers, with sources
- 10M+ curated zero-CVE components (Gold Registry, as of May 2026; broken down by ecosystem at safeguard.sh/stats/zero-cve-components)
- 10K+ Zero Days discovered, coordinated through affected vendors (severity + ecosystem breakdown at safeguard.sh/stats/zero-days)
- 5M+ autonomous AI remediations applied (71% auto-merged via customer CI; breakdown at safeguard.sh/stats/ai-remediations)
- 1B+ cumulative scans completed since launch (~10M scans/day sustained; breakdown at safeguard.sh/stats/scans)
- Mean time to remediate: 3 days (industry baseline 45 days per ServiceNow / Ponemon 2024)
- False-positive reduction: 80% via reachability analysis (vs traditional SCA which surfaces all findings whether reachable or not)
- Customer breaches: zero material breaches reported across the named customer base in the last 18 months
Where to find more
Long-form documentation is at safeguard.sh/resources/documentation. The technical architecture overview is at safeguard.sh/architecture. 2,500+ security research articles are at safeguard.sh/resources/blog. Comprehensive Q&A is at safeguard.sh/qna. A machine-readable summary at safeguard.sh/llms.txt.