Jit vs Safeguard: Security Plans as Code Over Open Source Scanners, or One Engine That Opens the Fix Pull Request (2026)

Jit and Safeguard both promise to take a messy application security stack and make it manageable, and both will show you a dashboard that looks tidy in a demo. The difference is in what sits underneath. Jit's model, as its documentation and marketing describe it, is orchestration: it packages well known open source scanners into security plans that run on your repositories, then layers triage and ticketing agents over the results. Safeguard's model is a set of its own engines for supply chain risk, with reachability analysis deciding which findings matter and an auto-fix engine opening tested pull requests for the ones that do.

This post compares the two on the points that decide a real evaluation. It is written for an AppSec lead or platform engineer who has Jit on a shortlist, or a contract, and wants to understand where each approach is strong before spending a month on a proof of concept. One piece of context first: Torq announced on 19 May 2026 that it had acquired Jit, and the coverage we read does not say what happens to the standalone application security product. We cover that in a companion guide, Jit after the Torq acquisition. This post stays on the technology.

A note on method. Facts about Jit come from its documentation, product pages, announcements and its AWS Marketplace listing, plus labeled third-party sources, all accessed on 28 October 2026. Jit's own site blocked our automated fetches that day, so several Jit documentation facts were read through search-engine excerpts of the page, and we mark them. Safeguard is our product, so treat our descriptions as a vendor's descriptions. We state Safeguard limitations only where our public docs state them, and we link the docs.

Key takeaways

• Different architectures. Jit orchestrates named open source scanners through security plans described as code, with a managed execution layer and agents on top. Safeguard runs first-party engines for dependency, code and dynamic testing, with an optional catalog of third-party engines.
• Transparency versus depth. Because Jit names its scanners, you can inspect and reproduce what it runs. Safeguard's reachability and fix engines are deeper in one area, supply chain risk, and less of a toolbox.
• Fixes are the sharpest split. Jit's agents triage, ticket and suggest remediation per its materials. Safeguard's auto-fix opens tested pull requests, off by default and set category by category, and Gold covers the case where upstream has no fix.
• Coverage claims differ by page. Jit's own pages describe static analysis coverage in different ways, from a documented Semgrep language list to a marketplace listing that cites 29 languages. Test the languages you use.
• Data handling and deployment diverge. Jit is a SaaS platform. Safeguard runs as SaaS in US, EU (Frankfurt) and Gov regions, in a private VPC, on premises or air-gapped, and can run with no LLM at all.
• They can coexist. Jit's open scanners and Safeguard's reachability and fixes do not conflict, provided one source is authoritative for each vulnerability.

What Jit is, as of 28 October 2026

Jit was founded in 2021 and describes itself on its GitHub organization as "the only open DevSecOps Orchestration Platform," a phrase that is Jit's own and which we cannot verify. By its 2025 launch materials it had added an agent layer, and in May 2026 it was acquired by Torq, which says it will fold Jit's context graph technology into its AI SOC platform (Torq, SiliconANGLE).

The product has four layers, and it helps to name them because the comparison with Safeguard is different at each one.

1. The scanner layer. Open source engines run as steps inside workflows.
2. The plan layer. Security plans decide which scanners and controls apply, where and when.
3. The experience layer. Findings reach developers in pull requests, the IDE and chat, and reach AppSec in a dashboard and tickets.
4. The agent layer. Agents prioritize, ticket, explain and report using a company knowledge graph.

Layer 1: the scanners

Per Jit's scanner summary, read through search excerpts on 28 October 2026, the platform runs the following.

Jit's flexible tooling page adds that Jit layers its own rules on top of Semgrep and that the platform supports open source, cloud native and commercial tools, including in-house ones. Jit's AWS Marketplace listing describes a "pluggable scanner architecture for custom or replacement scanners." A third-party comparison from Fluid Attacks, a competitor, lists the same open source tools and adds Grype and Syft for SBOMs, Prowler and AWS Security Hub for cloud posture, and Kubescape for Kubernetes (Fluid Attacks). That list is the competitor's reading, so use it as a pointer.

Two honest notes. First, the AWS Marketplace listing, which Jit publishes, describes static analysis across 29 languages and also names IAST and mobile testing. The Semgrep language list in the docs excerpt has 14 entries, and Fluid Attacks reports 14 languages. These can all be true if the listing counts languages through additional rule sets, but you should not assume it. Second, a security tool is only as good as its rules for your frameworks, so the test is to run it on your code.

Layer 2: security plans as code

The plans documentation describes a security plan as an outcome driven blueprint, "security plan as code." It captures requirements as intent, allows custom risks, and translates intent into running workflows and evaluated policies. Plans are broken into items across the layers of the stack, and workflows are triggered by pull requests, deployments, scheduled events or external inputs. Each item contains workflows, which contain jobs, which contain steps, and a step executes a security tool.

The Max plan page lists items with a coverage area, an SDLC stage, a name and a description, for example static code analysis at the code build stage. Beyond Max, Jit documents a minimum viable security plan built on open source tools and a GitHub security plan, and its materials and third-party profiles list plans for SOC 2 and the AWS Foundational Technical Review.

The plan modification docs show how customization works in practice: a plan file named jit-plan.yml holds an override section in which workflows are grouped by category, then jobs, then steps, each step naming a registry image and arguments. The excerpt we read says modifications are currently supported for Semgrep Python static analysis and KICS infrastructure as code misconfiguration scanning. If your plan depends on custom behavior elsewhere, ask what is supported today.

This design has a real virtue. The plan is a readable artifact with a shape you can reason about, and because the steps reference container images and arguments, the work is inspectable.

Layer 3: the developer experience

Third-party profiles say a pull request bot posts findings as comments, that IDE plugins exist for VS Code, IntelliJ and Cursor, and that Jit integrates with GitHub, GitLab, Bitbucket, Azure DevOps, major clouds, Slack, Teams, Jira and Linear, plus Drata and Vanta for compliance (AppSecSanta). The same profile says scans run on Jit's managed infrastructure rather than in your pipelines, so they do not consume CI minutes. Jit's pricing page, per excerpts, mentions self-hosted GitHub runners and webhooks, so the execution model may differ by plan. Reviewers on the marketplace praise setup ease and CI and GitHub integration.

Layer 4: agents and the knowledge graph

On 8 April 2025 Jit launched three agents (Help Net Security, PR Newswire).

• The AppSec Agent builds and updates risk assessments and threat models, flags critical risks and does contextual code review based on application architecture.
• The Compliance Agent maps environments against standards and regulations, produces compliance reports on demand and suggests how to close gaps.
• The Security Ops Agent automates triage and remediation by creating and following up on tickets, informing developers of risks and generating posture reports.

Jit says these draw on a company knowledge graph that combines findings, business priorities, SLAs and code to cloud context. A third-party profile describes three agent types, core, pre-built and custom, following a plan, execute, reflect and respond loop; that is a secondary description. Jit also announced an MCP server in preview for existing customers so that developers can read findings and act on them from an IDE such as Cursor. The announcement we found is from 2025, so ask about current status.

What Jit does well

If you are comparing honestly, these are Jit's strengths.

A fast, legible baseline. A team with no security tooling can connect a repository and get secrets scanning, static analysis, dependency checks and infrastructure as code checks without assembling it. The plan structure explains what runs.

Open, named scanners. Semgrep, Gitleaks, Trivy and friends are widely known. Your security reviewers recognize them, you can test them independently, and a plan does not hide behind a proprietary engine name.

Outcome oriented plans. A plan for SOC 2 or the AWS Foundational Technical Review, or for GitHub security best practices, maps tools to a goal rather than to a scanner list. That framing helps small teams that do not want to translate a framework into checks themselves.

Beyond application code. Cloud posture and GitHub organization settings are in scope per Jit's plans and third-party profiles, so the same platform can speak to build and posture.

Managed execution. If your pipelines are slow or your CI minutes are scarce, scans that run elsewhere are a relief.

Agents for toil. Ticket creation, follow-up and posture reports are real work that few AppSec teams enjoy. An agent that does it competently saves hours.

A wide integration footprint. Source control, cloud, chat and ticketing tools are covered per profiles, as are IDEs.

How Safeguard approaches the same job

Safeguard is a software supply chain security platform with four products: ESSCM for the full lifecycle, a Portal that includes a free entry tier and sharing of SBOMs and a Trust Center, TPRM for third-party risk, and OSM for open source inventory and the Gold directory. Here is how the pieces line up against Jit's four layers.

Engines

Safeguard's SCA engine resolves the transitive dependency graph, matches advisories enriched with EPSS and CISA KEV, and detects typosquats and dependency confusion. Its application security testing page covers first-party SAST, where findings include the full dataflow trace with CWE and OWASP mapping, and DAST against verified targets, where target ownership must be proven by a DNS TXT record, a file upload, a meta tag or a verified email domain before active checks run, with allow-listed scope, mandatory rate limits and audit logging. It also covers API testing from Postman collections. Secrets scanning, license compliance, SBOM generation in SPDX and CycloneDX, malware detection and attestation checks sit in the same findings model. The engine catalog also includes optional third-party engines, so Safeguard is not a purist single-engine product either.

Today, the docs list SAST for JavaScript and TypeScript, Python and Java, with more planned, so compare that with your languages (docs). Where your need is broad first-party language coverage with custom rules, Jit's Semgrep based approach is worth testing directly.

Reachability

Safeguard's reachability analysis assigns each finding one of four verdicts: reachable, conditionally reachable, unreachable or unknown, using call graphs and configuration analysis. It reports unknown where it cannot decide, rather than guessing. The goal is that the first pull requests you merge are the ones where the vulnerable code path is actually called. We cut noise this way, and we ask you to verify the effect on your repositories rather than accept a percentage from us.

Jit's materials say its context graph lets agents judge reachability and impact, and Fluid Attacks reports that Jit claims reachability without detail. The public sources we read do not document how Jit determines it, so ask for a worked example on one of your services.

Policies, gates and CI

Safeguard's policies and gates are rules that evaluate SBOMs and generate findings. Conditions include severity, CVSS score, EPSS score, license, component age, maintenance status and SLSA level. Each policy can block, warn or notify. Gates are checkpoints that enforce policies in CI, and exceptions require a justification and expiry date with approvers recorded in an audit trail. Policies are built in a visual builder or YAML, and Safeguard can generate policies from plain English prompts. Pre-built templates include no critical vulnerabilities, license compliance, SLSA Level 2 and no abandoned packages.

In CI, the documentation shows a CLI step for GitHub Actions, GitLab CI, Bitbucket Pipelines, Jenkins and Azure Pipelines, and any CI that can run a shell step. The step safeguard ci scan --wait --fail-on-gate all fails the job when a gate does not pass, --fail-on applies a severity threshold to dependency findings, and --local runs the scan on your runner so that only results leave it. Compare how results reach the pull request in your own trial, since that experience is a core part of what Jit sells.

Fixes

Safeguard's auto-fix opens tested, policy gated pull requests that move a dependency to a safe version, on one repository or in bulk, ranked by reachability. Remediation covers several categories, each with its own strategy, and autonomous remediation ships off, every category set to Manual, so you decide how much runs without a human. Safeguard's fix strength is dependencies, configuration and the open source components you ship. Where there is no upstream fix, Gold takes over, covered in the next section.

Agents and MCP

Safeguard's own models handle remediation and reasoning (Griffin), discovery and adversarial disproof (Eagle) and compliance narrative (Lion). They are optional: Zero AI mode runs the platform deterministically with no LLM, and you can bring your own model or run models inside your environment. The platform is also drivable by external assistants through the MCP server at mcp.safeguard.sh, which is listed in the Claude connector directory.

Fix it without waiting for upstream

Whatever the vulnerability, most of the risk sits in two gaps. The first is the time between a fix being published and that fix actually running in your production builds. The second, and worse, is the time before any fix exists at all, while a maintainer works out a patch and a release schedule. Waiting through both gaps is a habit, not a requirement. Safeguard gives teams three ways to close them.

1. Autonomous remediation: pull requests, not tickets

When a fixed version exists, Griffin, Safeguard's security model, opens a policy gated pull request that moves the affected dependency to the safe version, and your own CI runs your tests on it before anyone merges. You can run it on one repository or in bulk across every project that carries the vulnerable version. Remediation categories include dependencies, licenses, security posture and findings, and each one is set separately. Reachability analysis ranks the vulnerable code paths your application actually calls first, so the first pull requests you merge are the ones that matter most. Auto-merge is a separate switch and stays off by default, so your team decides what merges on its own and what still needs a human approval.

2. Gold Open Source: the safe version in seconds, free

gold.safeguard.sh is free and needs no login. Search any CVE or package to see affected and fixed versions, CVSS, EPSS, CISA KEV status and a recommended safe version, or use the "Am I affected?" checker. The same data is available through a free read API, README status badges and RSS threat feeds.

3. Gold Registry: start from components that are already fixed

Safeguard Gold rebuilds patched versions of open-source packages and images from source, signs each build, attaches an SBOM, SLSA provenance and VEX, and serves them from registry.safeguard.sh to your organization. Every Gold artifact is held continuously to zero exploitable critical or high CVEs and a completed license check, with signed build provenance (signed with Safeguard's key and verifiable with cosign, SLSA Build Level 3 as the design target) and a public evidence page you can hand to an auditor. An artifact loses its Gold badge if it regresses. Teams can point their own package directory at the registry so hardened equivalents resolve without changing how developers install anything.

When upstream has no fix yet, or will not ship one, Safeguard first works to get the fix accepted upstream. If the issue is severe or upstream will not move, Safeguard builds a fork, fixes the vulnerability there and publishes a hardened Gold build, so you are not stuck on a maintainer's release schedule. You can also request a hardened build of a specific package. Ask which packages it covers before you plan around it.

One honest limit: when the vulnerable code lives in a vendor appliance, a hosted service or an operating system runtime you do not build yourself, the fix still has to come from that vendor or distribution. The steps above apply to the open source dependencies and container images you build and ship, which is where most teams carry the largest and least tracked share of their exposure.

Comparison table, as of 28 October 2026

Jit facts come from its docs, pages and listing as read on 28 October 2026, and from labeled third-party profiles. Where a cell says "per" a source, it is that source's statement.

Orchestration or engine: the decision underneath

Here is the real question, put plainly. Do you want one tool to coordinate many scanners, or one engine that goes deep on the problem that hurts most?

What orchestration gets right

Orchestration admits that security is a set of different jobs. Secrets detection, dependency analysis, static analysis, container checks and cloud posture are different technical problems, and the best open source tool for each is often not the same tool. A good orchestrator picks them, wires them in, runs them at the right moment and presents one result. When the scanners are open, you can inspect their rules, run them outside the platform and replace one without replacing everything. That is a strong exit story, and it is Jit's most defensible design property.

What orchestration costs

Three costs deserve a test, not an assumption.

Duplicate and conflicting findings. Two scanners that look at the same dependency tree can report the same CVE with different severities, different fix versions and different file paths. A platform has to deduplicate across them and decide which description wins. Ask to see this on a repository that has overlapping coverage, such as a container image with application dependencies.

Severity is not exposure. Most open source scanners rank by CVSS. A CVSS 9.8 in a library function your code never calls is not the same as a CVSS 7.5 in a function on your request path. The prioritization layer, whether a knowledge graph or a reachability engine, is where orchestration either earns its keep or does not. Jit points to its graph. Safeguard points to reachability verdicts. Both are claims to verify, and the test is below.

Fix quality is a separate engineering problem. Finding a vulnerable version is the easy half. Choosing a target version that does not break your build, updating the lockfile correctly, running your tests and presenting a mergeable change is the hard half. An orchestrator that wraps scanners inherits each scanner's idea of a fix, which is often only a version number in a report. An engine built around fixes can test the change before it asks a developer to read it.

What an engine costs

Depth in one domain trades against breadth. Safeguard is strongest on supply chain risk, and a team that wants first-party code analysis with a large library of community and custom rules may find a Semgrep based platform closer to what it wants today. A single vendor's engine also asks you to trust its detection logic without the rule-level transparency of an open tool. Safeguard answers with documented reachability, standard SBOM exports, the optional third-party engine catalog and the local runner, so that your data and verdicts are portable, but that is an argument you should test rather than accept.

A way to settle it

Count where your actual risk comes from. If most of your incidents and audit findings come from secrets in repositories, misconfigured infrastructure and basic static analysis gaps, an orchestrated bundle of open tools covers the ground efficiently. If most of your backlog is vulnerable dependencies, transitive packages you cannot upgrade, and containers built on bases you do not control, depth in reachability and fixes pays back faster.

How to test finding quality, noise and fixes

Run the same tests on both, on code you know. A short method.

Seed a test repository. Take a service you understand and plant: one dependency with a high severity advisory on a code path your app calls; one with a high severity advisory in a function it never calls; a transitive vulnerable package two levels down; a test secret in a commit that you later removed; a Dockerfile with a base image that has known CVEs; an infrastructure as code file with an obvious misconfiguration. Write down what a perfect tool would say about each.

Score findings. For each tool, record whether each planted issue was found, how it was labeled, and what the severity and fix advice said. Count findings on code you did not plant and judge a sample by hand. That gives you false positive rates from your own code, not a marketing figure.

Check deduplication. Note whether the platform presents one finding or several for the same vulnerability seen by different scanners.

Check prioritization. Ask each tool to explain why the called vulnerability ranks above the uncalled one. If the answer is a score, ask what the score is made of. If it is a verdict, ask for the evidence. Safeguard reports reachability verdicts, including unknown. Ask Jit to show how its graph reached a conclusion for a specific finding.

Check the fix. For three vulnerable dependencies, compare what each tool gives you. A report line, a ticket, a suggested version, or a pull request that built and passed your tests? Count the minutes from finding to merged change. For Safeguard, the auto-fix docs describe the flow. For Jit, ask how its agent generated remediation works for your languages and what it tests.

Check the pipeline. Measure scan time added to a pull request, and where scans run. A managed execution model removes CI minutes and adds a dependency on the vendor's queue. A runner based model uses your minutes and keeps code on your side. Safeguard's --local mode keeps source on the runner and sends only results.

Check noise at the developer. Ask three developers to triage a week of findings from each tool, and listen. The best scanner in the world fails if people mute it.

Data, AI and deployment

Write down the questions you would put in a security review, then ask both vendors.

Where does code go? Jit is a SaaS platform. Third-party profiles say it may process source code metadata, scanner output (findings, severities, file paths) and cloud telemetry for the knowledge graph, and recommend reviewing its data handling during procurement. Another profile says Jit is probably not a fit if you must run all tooling on premises or in your own cloud accounts. Both are secondary, so ask Jit directly, and ask what changed after the Torq acquisition. Fluid Attacks lists Jit's deployment as SaaS in a US region.

Safeguard runs as SaaS in US, EU (Frankfurt) and Gov regions, in a private VPC or dedicated tenancy, on premises, or air-gapped, with models running inside your environment from signed offline snapshots. The local runner pulls the repository on your machine, scans there and pushes only encrypted results.

Does an LLM see it? Jit's agents are a core part of its current product, so ask what data each agent sees and which model providers process it. Safeguard's model mode is set per tenant: Full AI with its own models, Zero AI mode with no LLM at all, your own model, or quantized local models. If your rule is that no third-party model sees your code, write it down and ask each vendor to describe each feature under it.

Who audits the vendor? Third-party profiles report SOC 2 Type II for Jit. Ask for the current report and whether it covers the entity and systems you will rely on. Safeguard is SOC 2 Type I and Type II audited, with reports available on request, and its architecture is designed for FedRAMP High.

Pricing and total cost

Do not compare list prices; compare quotes on the same repositories.

Jit. Its pricing page shows plans and, as best our excerpts show, an annual per developer structure with an enterprise tier, with DAST described by third-party sources as consumption based. Its AWS Marketplace listing sells contract tiers by developer count over 12, 24 and 36 month terms with unlimited scans and remediation included, and charges for developers above a tier. Fluid Attacks reports a five developer minimum and an annual minimum term. A third-party comparison dated 30 September 2026 says pricing is quote-led since the Torq deal. We do not quote figures because sources disagree, so read the live page and get a written quote.

Safeguard. There is a Free plan with no card and no expiry. See safeguard.sh/pricing for current plans.

Total cost. Add the time your people spend on three things: triaging noise, writing and merging fixes, and maintaining the integration. A platform that costs more per developer and removes a day a week of triage from two engineers may be cheaper. A cheaper platform that you bolt three more tools onto may not be. Price the full toolchain you would run under each option, including any commercial scanners you keep.

When to choose Jit

• You want a managed baseline of well known open source scanners and plans that read like a checklist, without assembling it.
• You value transparent engines and a plan format you can inspect and, to the extent the docs support it, modify.
• Your first goals are SOC 2 or AWS Foundational Technical Review readiness, GitHub security hygiene and a security posture for a small team.
• You want pull request comments and IDE plugins to be the center of the developer experience, and scans off your CI minutes.
• You are or plan to be a Torq customer and want the context graph and security operations in one relationship.
• You are comfortable with a SaaS-only model and with the status questions in our companion guide answered to your satisfaction.

When to choose Safeguard

• Your pain is dependencies: you want reachability verdicts and tested pull requests that fix, ranked so the important ones come first.
• Upstream does not fix some of what hurts you and you want a route that does not end in waiting: rebuilt components, fork and patch, or a requested hardened build.
• You need control over data and models: Zero AI mode, your own model, local models, a private VPC, on premises or air-gapped deployment, or a local runner.
• You want third-party risk, SBOMs, compliance evidence and zero day advisories in one findings model alongside your scanner.
• You want install-time protection: the Package Firewall can block malicious, typosquatted and dependency confusion packages. We did not find install-time package blocking described in the Jit sources we read, so ask Jit about it if it matters to you.
• You want assistants to drive the platform through MCP.

Both. A workable pattern is to keep Jit's plan for baseline secrets, static analysis and infrastructure checks, and add Safeguard for dependency reachability, fixes and evidence. Decide which tool is authoritative for dependency verdicts, so developers do not triage the same CVE twice.

Evaluation checklist: a two week proof of concept

Before you start

• Name the three repositories: typical, legacy and messy.
• Write the six planted issues from the test method above and the expected verdict for each.
• List your languages and frameworks and check each against both vendors' documentation.
• Decide the success measures: planted issues found, false positives in a sample, minutes to a merged fix, developer rating.

Week 1: connect and scan

• Connect the repositories to each tool. For Safeguard, connect through integrations or run the CLI locally.
• Run each on pull requests and on the default branch.
• Record time to first result and the scan time added to a pull request.
• Export the findings list from each in CSV or JSON.

Week 2: fix and gate

• Pick three vulnerable dependencies and drive each to a merged change using each tool, counting minutes.
• Write one blocking policy in each (no critical vulnerabilities, plus one for license) and watch it fail a build on purpose.
• Test an exception: request it, approve it, check it expires.
• Ask each vendor your data, AI and audit questions in writing.

Decision

• Compare against the success measures, not the demo.
• Check total cost from written quotes on the same scope.
• If you would use both, decide who owns dependency verdicts and write it into your runbook.

Frequently asked questions

What is the difference between Jit and Safeguard?

Jit orchestrates open source scanners through security plans and adds agents on a context graph. Safeguard runs its own engines for supply chain risk, uses reachability to rank findings, and opens tested fix pull requests, with deployment from SaaS to air-gapped. Jit is a quick, plan driven baseline over named open tools. Safeguard goes deeper on dependencies, fixes and evidence.

Does Jit run Semgrep?

Per Jit's scanner summary, as read on 28 October 2026, yes: it uses Semgrep for static analysis, and its flexible tooling page says Jit adds its own rules on top of Semgrep. Other named scanners include Gitleaks, TruffleHog, Trivy, Gosec, OSV-Scanner and KICS.

Does Jit do reachability analysis?

Third-party profiles say Jit claims reachability, and Jit's materials say its context graph lets agents judge reachability and impact. We did not find documentation of the method. Ask for a worked example on one of your services. Safeguard documents four verdicts: reachable, conditionally reachable, unreachable and unknown.

Does Jit open fix pull requests?

Jit's materials describe agents that automate triage and remediation, create and follow up on tickets and, per marketplace copy, support in-PR security testing and automated fixes. Ask what is tested before a change reaches a developer and in which languages. Safeguard's auto-fix opens tested, policy gated pull requests, and ships off by default.

Is Jit still independent?

No. Torq announced it had acquired Jit on 19 May 2026. Torq says it will add Jit's context graph to its AI SOC platform "without adding a separate product line for teams to adopt." The coverage we read does not say what happens to the standalone application security product. Our companion guide lists the questions to ask.

Can I use Jit and Safeguard together?

Yes. Keep Jit for baseline scanner coverage and use Safeguard for reachability, fix pull requests, SBOMs and evidence. Pick one authoritative source for dependency verdicts so findings are not duplicated.

Do I need an LLM to use Safeguard?

No. Zero AI mode runs the platform deterministically with no LLM. You can also bring your own model or run models in your environment.

Does Safeguard support the languages my team uses?

For dependency scanning, check the SCA docs, and for reachability the reachability docs. For static analysis, today the docs list JavaScript and TypeScript, Python and Java, with more planned. Run a proof of concept on your own repositories.

Is Safeguard SOC 2 audited?

Safeguard is SOC 2 Type I and Type II audited, with reports available on request, and its architecture is designed for FedRAMP High. Ask any vendor, Jit and Torq included, for reports under NDA.

What happens when a vulnerability has no upstream fix?

Safeguard first works to get a fix accepted upstream. If the issue is severe or upstream will not move, it builds a fork, fixes the vulnerability there and publishes a hardened Gold build, and you can request a compatibility tested build of a specific package. Ask which packages are covered before you plan around it.

Where to go next

The quickest test of Safeguard's side is one repository. The CLI and local runner scan on your machine and send only encrypted results, reachability and auto-fix are documented in detail, and Gold is free to browse. Related reading: Jit after the Torq acquisition, what is security as code, Semgrep and CodeQL compared, Trivy and Grype compared, consolidating AppSec tools with an ASPM platform and reachability as the bridge between SCA and fix pull requests. The full source list, accessed on 28 October 2026, is in this post's metadata.

Third party facts were checked against public pages on 28 October 2026 and can change. If something here is out of date or wrong, tell us and we will fix it. Jit, Torq, Semgrep, Gitleaks, TruffleHog, Trivy, Gosec, OSV-Scanner, KICS, OWASP ZAP, Syft, Grype, Prowler, Kubescape, GitHub, AWS, Drata and Vanta are trademarks of their owners, used here in plain text for comparison with no implied partnership or endorsement.