Is Pytsa veilig?
Pytsa — Nerq Trust Score 72.2/100 (B-beoordeling). Op basis van analyse van 5 vertrouwensdimensies wordt het beschouwd als over het algemeen veilig maar met enkele zorgen. Laatst bijgewerkt: 2026-07-16.
Ja, Pytsa is veilig om te gebruiken. Pytsa is een software tool met een Nerq Vertrouwensscore van 72.2/100 (B), based on 5 onafhankelijke gegevensdimensies. Aanbevolen voor gebruik. Beveiliging: 0/100. Onderhoud: 1/100. Populariteit: 0/100. Gegevens afkomstig van meerdere openbare bronnen waaronder pakketregisters, GitHub, NVD, OSV.dev en OpenSSF Scorecard. Laatst bijgewerkt: 2026-07-16. Machineleesbare gegevens (JSON).
Is Pytsa veilig?
YES — Pytsa has a Nerq Trust Score of 72.2/100 (B). Voldoet aan de vertrouwensdrempel van Nerq met sterke signalen op het gebied van beveiliging, onderhoud en gemeenschapsacceptatie. Aanbevolen voor gebruik — bekijk het volledige rapport hieronder voor specifieke overwegingen.
Wat is de vertrouwensscore van Pytsa?
Pytsa heeft een Nerq Trust Score van 72.2/100 met het cijfer B. Deze score is gebaseerd op 5 onafhankelijk gemeten dimensies, waaronder beveiliging, onderhoud en community-adoptie.
Wat zijn de belangrijkste beveiligingsbevindingen voor Pytsa?
Het sterkste signaal van Pytsa is naleving met 100/100. Er zijn geen bekende kwetsbaarheden gedetecteerd. It meets the Nerq Verified threshold of 70+.
Wat is Pytsa en wie onderhoudt het?
| Ontwikkelaar | nikpau |
| Categorie | Data |
| Sterren | 17 |
| Bron | https://github.com/nikpau/pytsa |
Naleving van regelgeving
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdicties |
Populaire alternatieven in data
What Is Pytsa?
Pytsa is a software tool in the data category: PyTSA is a Python framework for vessel monitoring and trajectory extraction from AIS records.. It has 17 GitHub stars. Nerq Trust Score: 72/100 (B).
Nerq independently analyzes every software tool, app, and extension across multiple trust signals including beveiliging vulnerabilities, onderhoud activity, license naleving, and gemeenschapsacceptatie.
How Nerq Assesses Pytsa's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensies. Here is how Pytsa performs in each:
- Beveiliging (0/100): Pytsa's beveiliging posture is poor. This score factors in known CVEs, dependency vulnerabilities, beveiliging policy presence, and code signing practices.
- Onderhoud (1/100): Pytsa is potentially abandoned. We track commit frequency, release cadence, issue response times, and PR merge rates.
- Documentation (1/100): Documentation quality is insufficient. This includes README completeness, API documentatie, usage examples, and contribution guidelines.
- Compliance (100/100): Pytsa is broadly compliant. Assessed against regulations in 52 jurisdicties including the EU AI Act, CCPA, and GDPR.
- Community (0/100): Community adoption is limited. Gebaseerd op GitHub stars, forks, download counts, and ecosystem integrations.
The overall Trust Score of 72.2/100 (B) reflects the weighted combination of these signals. This exceeds the Nerq Verified threshold of 70, indicating the tool meets our standards for production use.
Who Should Use Pytsa?
Pytsa is designed for:
- Developers and teams working with data tools
- Organizations evaluating AI tools for their stack
- Researchers exploring AI capabilities in this domain
Risk guidance: Pytsa meets the minimum threshold for production use, but we recommend monitoring for beveiliging advisories and keeping dependencies up to date. Consider implementing additional guardrails for sensitive workloads.
How to Verify Pytsa's Safety Yourself
While Nerq provides automated trust analysis, we recommend these additional steps before adopting any software tool:
- Check the source code — Bekijk de repository's beveiliging policy, open issues, and recent commits for signs of active onderhoud.
- Scan dependencies — Use tools like
npm audit,pip-audit, orsnykto check for known vulnerabilities in Pytsa's dependency tree. - Beoordeling permissions — Understand what access Pytsa requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Pytsa 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=pytsa - Bekijk de license — Confirm that Pytsa'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 beveiliging concerns openly. Low community engagement may indicate limited peer review of the codebase.
Common Safety Concerns with Pytsa
When evaluating whether Pytsa is safe, consider these category-specific risks:
Understand how Pytsa processes, stores, and transmits your data. Bekijk de tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Pytsa's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher beveiliging risk.
Regularly check for updates to Pytsa. Beveiliging patches and bug fixes are only effective if you're running the latest version.
If Pytsa 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 Pytsa's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Pytsa in violation of its license can expose your organization to legal liability.
Pytsa and the EU AI Act
Pytsa 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 naleving assessment covers 52 jurisdicties worldwide. For organizations deploying AI tools in regulated environments, understanding these classifications is essential for legal naleving.
Best Practices for Using Pytsa Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Pytsa while minimizing risk:
Periodically review how Pytsa is used in your workflow. Check for unexpected behavior, permissions drift, and naleving with your beveiliging policies.
Ensure Pytsa and all its dependencies are running the latest stable versions to benefit from beveiliging patches.
Grant Pytsa only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Pytsa's beveiliging advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Pytsa is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Pytsa?
Even well-trusted tools aren't right for every situation. Consider avoiding Pytsa in these scenarios:
- Scenarios where Pytsa's specific capabilities exceed your actual needs — simpler tools may be safer
- Air-gapped environments where the tool cannot receive beveiliging updates
- Projects with strict regulatory requirements that haven't been explicitly validated
For each scenario, evaluate whether Pytsa's trust score of 72.2/100 meets your organization's risk tolerance. The Nerq Verified status indicates general production readiness, but sector-specific requirements may apply.
How Pytsa 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. Pytsa's score of 72.2/100 is significantly above the category average of 62/100.
This places Pytsa in the top tier of data tools that Nerq tracks. Tools scoring this far above average typically demonstrate mature beveiliging practices, consistent release cadence, and broad gemeenschapsacceptatie.
Industry benchmarks matter because they contextualize a tool's safety profile. A score that looks matig 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 Pytsa 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 onderhoud patterns change, Pytsa'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 beveiliging and quality. Conversely, a downward trend may signal reduced onderhoud, growing technical debt, or unresolved vulnerabilities. To track Pytsa's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=pytsa&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 — beveiliging, onderhoud, documentatie, naleving, and community — has evolved independently, providing granular visibility into which aspects of Pytsa are strengthening or weakening over time.
Pytsa vs Alternatieven
In the data category, Pytsa scores 72.2/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Pytsa vs firecrawl — Trust Score: 57.2/100
- Pytsa vs MinerU — Trust Score: 62.2/100
- Pytsa vs mindsdb — Trust Score: 47.8/100
Belangrijkste conclusies
- Pytsa has a Trust Score of 72.2/100 (B) and is Nerq Verified.
- Pytsa meets the minimum threshold for production deployment, though monitoring and additional guardrails are recommended.
- Among data tools, Pytsa scores significantly above the category average of 62/100, demonstrating above-average reliability.
- Always verify safety independently — use Nerq's Preflight API for automated, up-to-date trust checks before integration.
Veelgestelde vragen
Is Pytsa veilig?
Wat is de vertrouwensscore van Pytsa?
Wat zijn veiligere alternatieven voor Pytsa?
Hoe vaak wordt de beveiligingsscore van Pytsa bijgewerkt?
Kan ik Pytsa gebruiken in een gereguleerde omgeving?
Zie ook
Disclaimer: Nerq-vertrouwensscores zijn geautomatiseerde beoordelingen op basis van openbaar beschikbare signalen. Ze vormen geen aanbeveling of garantie. Voer altijd uw eigen verificatie uit.