Is Graf Ai Data Analysis Platform Safe?
Graf Ai Data Analysis Platform — Nerq Trust Score 54.1/100 (D grade). Score based on 5 independent trust signals.
Graf Ai Data Analysis Platform is a software tool with a Nerq Trust Score of 54.1/100 (D), based on 5 independent data dimensions. Security: 0/100. Maintenance: 1/100. Popularity: 0/100. Data sourced from multiple public sources including package registries, GitHub, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: n/a. Machine-readable data (JSON).
Is Graf Ai Data Analysis Platform safe?
Trust Score Breakdown — Graf Ai Data Analysis Platform has a Nerq Trust Score of 54.1/100 (D). Measured across 5 independent trust signals.
What is Graf Ai Data Analysis Platform's trust score?
Graf Ai Data Analysis Platform has a Nerq Trust Score of 54.1/100, earning a D grade. This score is based on 5 independently measured dimensions including security, maintenance, and community adoption.
What are the key security findings for Graf Ai Data Analysis Platform?
Graf Ai Data Analysis Platform's strongest signal is compliance at 92/100. No known vulnerabilities have been detected.
What is Graf Ai Data Analysis Platform and who maintains it?
| Author | graf-io |
| Category | Data |
| Source | https://github.com/graf-io/Graf---AI-Data-Analysis-Platform |
Regulatory Compliance
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 92/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Popular Alternatives in data
What Is Graf Ai Data Analysis Platform?
Graf Ai Data Analysis Platform is a software tool in the data category: Graf automates end-to-end data analysis with agentic AI and machine learning.. Nerq Trust Score: 54/100 (D).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including security vulnerabilities, maintenance activity, license compliance, and community adoption.
How Nerq Assesses Graf Ai Data Analysis Platform's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Graf Ai Data Analysis Platform performs in each:
- Security (0/100): Graf Ai Data Analysis Platform's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Maintenance (1/100): Graf Ai Data Analysis Platform is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (0/100): Documentation quality is insufficient. This includes README completeness, API documentation, usage examples, and contribution guidelines.
- Compliance (92/100): Graf Ai Data Analysis Platform is broadly compliant. Assessed against regulations in 52 jurisdictions including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Based on GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 54.1/100 (D) is the weighted combination of these measured signals. It is a measurement, not a pass/fail or suitability judgment — weigh the individual signals against your own requirements.
Who Typically Evaluates Graf Ai Data Analysis Platform?
Graf Ai Data Analysis Platform is commonly evaluated by:
- Developers and teams working with data tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
How to read the signals: Graf Ai Data Analysis Platform's measured signals (security 0/100, maintenance 1/100, documentation 0/100, community 0/100) are shown above. These are measurements, not a suitability judgment — weigh each signal against the requirements of your own use case and risk tolerance.
How to Verify Graf Ai Data Analysis Platform's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Review the repository's security policy, open issues, and recent commits for signs of active maintenance.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Graf Ai Data Analysis Platform's dependency tree. - Review permissions — Understand what access Graf Ai Data Analysis Platform requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Graf Ai Data Analysis Platform in a sandboxed environment before granting access to production data or systems.
- Monitor continuously — Use Nerq's API to set up automated trust checks:
GET nerq.ai/v1/preflight?target=Graf---AI-Data-Analysis-Platform - Review the license — Confirm that Graf Ai Data Analysis Platform's license is compatible with your intended use case. Pay attention to restrictions on commercial use, redistribution, and derivative works. Some AI tools use dual licensing or have separate terms for enterprise customers that differ from the open-source license.
- Check community signals — Look at the project's issue tracker, discussion forums, and social media presence. A healthy community actively reports bugs, contributes fixes, and discusses security concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Graf Ai Data Analysis Platform
When evaluating whether Graf Ai Data Analysis Platform is safe, consider these category-specific risks:
Understand how Graf Ai Data Analysis Platform processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Graf Ai Data Analysis Platform's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Graf Ai Data Analysis Platform. Security patches and bug fixes are only effective if you're running the latest version.
If Graf Ai Data Analysis Platform connects to external APIs or services, each integration point is a potential attack surface. Audit all third-party connections, verify that data shared with external services is minimized, and ensure that integration credentials are rotated regularly.
Verify that Graf Ai Data Analysis Platform's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Graf Ai Data Analysis Platform in violation of its license can expose your organization to legal liability.
Graf Ai Data Analysis Platform and the EU AI Act
Graf Ai Data Analysis Platform is classified as Minimal Risk under the EU AI Act. This is the lowest risk category, meaning it faces minimal regulatory requirements. However, transparency obligations still apply.
Nerq's compliance assessment covers 52 jurisdictions worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal compliance.
Best Practices for Using Graf Ai Data Analysis Platform Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Graf Ai Data Analysis Platform while minimizing risk:
Periodically review how Graf Ai Data Analysis Platform is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Graf Ai Data Analysis Platform and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Graf Ai Data Analysis Platform only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Graf Ai Data Analysis Platform's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Graf Ai Data Analysis Platform is used within your organization, including data handling guidelines and acceptable use cases.
Situations That Warrant Independent Review of Graf Ai Data Analysis Platform
Nerq's signals are one input. In the following situations, evaluate Graf Ai Data Analysis Platform's measured signals against your own requirements before making a decision:
- Environments handling sensitive or regulated data (healthcare, finance, government)
- Mission-critical systems where downtime has significant business impact
- Deployments with strict regulatory requirements that must be independently validated
For each situation, compare Graf Ai Data Analysis Platform's measured trust score of 54.1/100 and its individual signals against your organization's own criteria. Nerq does not assert whether Graf Ai Data Analysis Platform is suitable for any particular use.
How Graf Ai Data Analysis Platform Compares to Industry Standards
Nerq indexes over 6 million software tools, apps, and packages across dozens of categories. Among data tools, the average Trust Score is 62/100. Graf Ai Data Analysis Platform's score of 54.1/100 is near the category average of 62/100.
This places Graf Ai Data Analysis Platform in line with the typical data tool tool. It meets baseline expectations but does not distinguish itself from peers on trust metrics.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks moderate in isolation may actually represent strong performance within a challenging category — or vice versa. Nerq's category-relative analysis helps teams make informed decisions by showing not just absolute quality, but how a tool ranks against its direct peers.
Trust Score History
Nerq continuously monitors Graf Ai Data Analysis Platform and recalculates its Trust Score as new data becomes available. Our scoring engine ingests real-time signals from source repositories, vulnerability databases (NVD, OSV.dev), package registries, and community metrics. When a new CVE is published, a major release ships, or maintenance patterns change, Graf Ai Data Analysis Platform's score is updated within 24 hours.
Historical trust trends reveal whether a tool is improving, stable, or declining over time. A tool that consistently maintains or improves its score demonstrates ongoing commitment to security and quality. Conversely, a downward trend may signal reduced maintenance, growing technical debt, or unresolved vulnerabilities. To track Graf Ai Data Analysis Platform's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Graf---AI-Data-Analysis-Platform&include=history
Nerq retains trust score snapshots at regular intervals, enabling trend analysis across weeks and months. Enterprise users can access detailed historical reports showing how each dimension — security, maintenance, documentation, compliance, and community — has evolved independently, providing granular visibility into which aspects of Graf Ai Data Analysis Platform are strengthening or weakening over time.
Graf Ai Data Analysis Platform vs Alternatives
In the data category, Graf Ai Data Analysis Platform scores 54.1/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Graf Ai Data Analysis Platform vs firecrawl — Trust Score: 64.4/100
- Graf Ai Data Analysis Platform vs MinerU — Trust Score: 76.6/100
- Graf Ai Data Analysis Platform vs mindsdb — Trust Score: 68.1/100
Key Takeaways
- Graf Ai Data Analysis Platform has a measured Nerq Trust Score of 54.1/100 (D) — a composite of independent signals, not a suitability judgment.
- Among data tools, Graf Ai Data Analysis Platform scores near the category average of 62/100 (a positional measurement relative to peers).
- The individual signals — security, maintenance, documentation, compliance, community — are shown above. Weigh them against your own requirements.
- Query the current measured values via Nerq's Preflight API.
Frequently Asked Questions
Is Graf Ai Data Analysis Platform Safe?
What is Graf Ai Data Analysis Platform's trust score?
What are safer alternatives to Graf Ai Data Analysis Platform?
How often is Graf Ai Data Analysis Platform's safety score updated?
Can I use Graf Ai Data Analysis Platform in a regulated environment?
See Also
Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.