Je Data Analyst Deepagent bezpečný?
Data Analyst Deepagent — Nerq Trust Score 67.7/100 (Stupeň C). Na základě analýzy 5 dimenzí důvěryhodnosti je obecně bezpečný, ale s některými obavami. Naposledy aktualizováno: 2026-04-01.
Používejte Data Analyst Deepagent s opatrností. Data Analyst Deepagent is a software tool se skóre důvěryhodnosti Nerq 67.7/100 (C), based on 5 independent data dimensions. Je pod doporučeným prahem 70. 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: 2026-04-01. Strojově čitelná data (JSON).
Je Data Analyst Deepagent bezpečný?
OPATRNOST — Data Analyst Deepagent má skóre důvěryhodnosti Nerq 67.7/100 (C). Má střední signály důvěryhodnosti, ale vykazuje některé oblasti vyžadující pozornost. Vhodné pro vývojové použití — zkontrolujte bezpečnostní signály a signály údržby před nasazením do produkce.
Jaké je skóre důvěryhodnosti Data Analyst Deepagent?
Data Analyst Deepagent má Nerq skóre důvěryhodnosti 67.7/100 se stupněm C. Toto skóre je založeno na 5 nezávisle měřených dimenzích.
Jaká jsou klíčová bezpečnostní zjištění pro Data Analyst Deepagent?
Nejsilnější signál Data Analyst Deepagent je shoda na 100/100. Nebyly zjištěny žádné známé zranitelnosti. Dosud nedosáhl ověřeného prahu Nerq 70+.
Co je Data Analyst Deepagent a kdo jej spravuje?
| Autor | BoualamHamza |
| Kategorie | data |
| Zdroj | https://github.com/BoualamHamza/Data-analyst-DeepAgent |
| Frameworks | openai · anthropic |
| Protocols | rest |
Regulační shoda
| EU AI Act Risk Class | MINIMAL |
| Compliance Score | 100/100 |
| Jurisdictions | Assessed across 52 jurisdictions |
Populární alternativy v data
What Is Data Analyst Deepagent?
Data Analyst Deepagent is a software tool in the data category: AI-powered data analyst agent for querying databases, generating visualizations, and detecting anomalies.. Nerq Trust Score: 68/100 (C).
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 Data Analyst Deepagent's Safety
Nerq's Trust Score is calculated from 13+ independent signals aggregated into five dimensions. Here is how Data Analyst Deepagent performs in each:
- Bezpečnost (0/100): Data Analyst Deepagent's security posture is poor. This score factors in known CVEs, dependency vulnerabilities, security policy presence, and code signing practices.
- Údržba (1/100): Data Analyst Deepagent 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 documentation, usage examples, and contribution guidelines.
- Compliance (100/100): Data Analyst Deepagent 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 67.7/100 (C) reflects the weighted combination of these signals. This is below the Nerq Verified threshold of 70. We recommend additional due diligence before production deployment.
Who Should Use Data Analyst Deepagent?
Data Analyst Deepagent 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: Data Analyst Deepagent is suitable for development and testing environments. Before production deployment, conduct a thorough review of its security posture, review the specific trust signals above, and consider whether a higher-scored alternative meets your requirements.
How to Verify Data Analyst Deepagent'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 Data Analyst Deepagent's dependency tree. - Recenze permissions — Understand what access Data Analyst Deepagent requires. Software tools should follow the principle of least privilege.
- Test in isolation — Run Data Analyst Deepagent 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=Data-analyst-DeepAgent - Zkontrolujte license — Confirm that Data Analyst Deepagent'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 Data Analyst Deepagent
When evaluating whether Data Analyst Deepagent is safe, consider these category-specific risks:
Understand how Data Analyst Deepagent processes, stores, and transmits your data. Review the tool's privacy policy and data retention practices, especially for sensitive or proprietary information.
Check Data Analyst Deepagent's dependency tree for known vulnerabilities. Tools with outdated or unmaintained dependencies pose a higher security risk.
Regularly check for updates to Data Analyst Deepagent. Security patches and bug fixes are only effective if you're running the latest version.
If Data Analyst Deepagent 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 Data Analyst Deepagent's license is compatible with your intended use case. Some AI tools have restrictive licenses that limit commercial use, redistribution, or derivative works. Using Data Analyst Deepagent in violation of its license can expose your organization to legal liability.
Data Analyst Deepagent and the EU AI Act
Data Analyst Deepagent 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 Data Analyst Deepagent Safely
Whether you're an individual developer or an enterprise team, these practices will help you get the most from Data Analyst Deepagent while minimizing risk:
Periodically review how Data Analyst Deepagent is used in your workflow. Check for unexpected behavior, permissions drift, and compliance with your security policies.
Ensure Data Analyst Deepagent and all its dependencies are running the latest stable versions to benefit from security patches.
Grant Data Analyst Deepagent only the minimum permissions it needs to function. Avoid granting admin or root access.
Subscribe to Data Analyst Deepagent's security advisories and vulnerability disclosures. Use Nerq's API to get automated trust score updates.
Create and maintain a clear policy for how Data Analyst Deepagent is used within your organization, including data handling guidelines and acceptable use cases.
When Should You Avoid Data Analyst Deepagent?
Even promising tools aren't right for every situation. Consider avoiding Data Analyst Deepagent in these scenarios:
- Production environments handling sensitive customer data
- Regulated industries (healthcare, finance, government) without additional compliance review
- Mission-critical systems where downtime has significant business impact
For each scenario, evaluate whether Data Analyst Deepagent 67.7/100 meets your organization's risk tolerance. We recommend running a manual security assessment alongside the automated Nerq score.
How Data Analyst Deepagent 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. Data Analyst Deepagent's score of 67.7/100 is above the category average of 62/100.
This positions Data Analyst Deepagent favorably among data tools. While it outperforms the average, there is still room for improvement in certain trust dimensions.
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 Data Analyst Deepagent 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, Data Analyst Deepagent'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 Data Analyst Deepagent's score over time, use the Nerq API: GET nerq.ai/v1/preflight?target=Data-analyst-DeepAgent&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 Data Analyst Deepagent are strengthening or weakening over time.
Data Analyst Deepagent vs Alternatives
V kategorii data, Data Analyst Deepagent získal skóre 67.7/100. There are higher-scoring alternatives available. For a detailed comparison, see:
- Data Analyst Deepagent vs firecrawl — Trust Score: 73.8/100
- Data Analyst Deepagent vs MinerU — Trust Score: 84.6/100
- Data Analyst Deepagent vs mindsdb — Trust Score: 77.5/100
Hlavní závěry
- Data Analyst Deepagent má skóre důvěryhodnosti 67.7/100 (C) and is not yet Nerq Verified.
- Data Analyst Deepagent shows moderate trust signals. Conduct thorough due diligence before deploying to production environments.
- Among data tools, Data Analyst Deepagent scores 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.
Často kladené otázky
Je Data Analyst Deepagent bezpečný k použití?
Jaké je skóre důvěryhodnosti Data Analyst Deepagent?
Jaké jsou bezpečnější alternativy k Data Analyst Deepagent?
How often is Data Analyst Deepagent's safety score updated?
Mohu použít Data Analyst Deepagent v regulovaném prostředí?
Disclaimer: Skóre důvěryhodnosti Nerq jsou automatizovaná hodnocení založená na veřejně dostupných signálech. Nejsou doporučením ani zárukou. Vždy proveďte vlastní ověření.