Docusign AI contract — TermScout contract intelligence report

Docusign AI Attachment Review & Analysis

Docusign TermScout AI certified contract badge

AI Certification (Beta) Overview:

When TermScout certifies a contract, it means that agreement has met our rigorous standards and made the below commitments to their customers as it relates to their AI product or services.

Receiving Termscout's AI Certification (Beta) means that the product is on the cutting edge of AI development and shows they're committed to establishing trust and transparency with their customers and being a responsible corporate citizen when it comes to their AI development and functionalities.

Termscout acknowledges that presently AI is something of a moving target, but at this point, we believe responsible vendors make these minimum commitments and are continuously evaluating their practices.

AI Commitments Overview:

Review the AI-related commitments and view source language citations below.

Docusign has established guardrails related to the use of their AI offering.
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Docusign addresses how they use Customer data related to their AI offering.
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Docusign addresses how they retain Customer data as it relates to their AI offering.
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Customer retains ownership of input data submitted to Docusign as it relates to their AI offering.
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Docusign commits to notifying Customer of a security breach impacting the Customer's data.
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Docusign commits to complying with applicable AI laws
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In conversations between TermScout and Docusign, Docusign has indicated their ongoing commitment to adopting contractual best practices related to AI as they're developed.
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AI Certification (Beta) Guiding Principles:

Termscout's AI Certification (beta) covers five major categories outlined below. Termscout believes while there are other important principles related to AI, these five categories cover the major obligations related to AI found in vendor contracts today and expect over time this will expand to fully realized compliance index.

1. Use Restrictions: Limiting harmful or unintended uses of AI Systems.
2. Transparency and Explainability: Ensuring users understand how AI works and its limitations.
3. Data Usage and Ownership: Clarifying rights around training data and generated content.
4. Security and Incident Response: Protecting data and systems from breaches or misuse.
5. Regulatory Compliance: Ensuring alignment with evolving laws.

AI Certification (Beta) Program:

If you are interested in being part of the AI Certification (Beta) Program and joining a standing group of select customers who has ongoing conversations with us about these standards and how they should evolve over time, please contact us at sales@termscout.com

Frequently Asked Questions

Find quick answers to the most common questions about our platform, process, and agreements.

Legal teams often escalate agreements when model training provisions are drafted broadly enough to permit reuse of customer prompts, outputs, or operational data beyond service delivery purposes. Escalation is especially common when the agreement lacks clear restrictions around retention periods, anonymization standards, subprocessors, or downstream commercial use. Enterprise counsel also tends to scrutinize language that combines service improvement rights with unrestricted model development authority, since the practical boundary between the two may be unclear once deployed operationally.

Traditional analytics clauses generally focus on aggregated service optimization, while AI model training rights can create long-term governance and competitive concerns that extend beyond ordinary product telemetry. Buyers increasingly evaluate whether customer data could influence shared models, improve capabilities for competitors, or generate outputs outside the intended commercial relationship. As a result, legal review often centers on scope limitation, reuse boundaries, and operational safeguards rather than generic confidentiality language alone. This has become a significant source of negotiation friction in AI vendor procurement.

Agreements are generally viewed more favorably when they distinguish clearly between operational service functionality and broader model development activities. Enterprises typically respond well to narrowly tailored rights, explicit exclusions for confidential or regulated information, and transparent descriptions of how data may be processed. Vendors also reduce escalation pressure when they explain retention practices, permit customer opt-outs, or avoid ambiguous “improvement” language that could later support expansive reuse interpretations. Clarity and proportionality tend to matter more than aggressive ownership positioning.

Counsel increasingly evaluates AI training clauses through a governance and downstream exposure lens rather than treating them as isolated IP provisions. Review often includes questions about regulatory obligations, customer commitments, auditability, data lineage, and reputational risk if sensitive information enters shared training environments. Operational concern rises when agreements do not explain how training datasets are segregated, how deletion requests are handled, or whether customer content could persist indirectly inside future model behavior. These issues frequently require coordination across legal, privacy, security, and procurement stakeholders.