Atlassian AI contract — TermScout contract intelligence report

Atlassian AI Terms Review & Analysis

Atlassian 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.

Atlassian has established guardrails related to the use of their AI offering.
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Atlassian addresses how they use Customer data related to their AI offering.
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Atlassian 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 Atlassian as it relates to their AI offering.
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Atlassian commits to notifying Customer of a security breach impacting the Customer's data.
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Atlassian commits to complying with applicable AI laws
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In conversations between TermScout and Atlassian, Atlassian 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.

Procurement teams often encounter delays when AI SaaS agreements include expansive data usage rights, vague model training permissions, or broad unilateral change clauses. Friction also increases when liability protections are weak relative to the sensitivity of customer data or the operational reliance on the platform. Enterprise buyers increasingly expect clear boundaries around how prompts, outputs, telemetry, and customer datasets are handled. Agreements that fail to distinguish between service delivery and model improvement activities typically require additional legal and security review before approval.

TrustMark evaluates whether AI-related provisions are transparent, commercially reasonable, and aligned with emerging enterprise expectations. Review factors may include restrictions on model training, ownership of customer inputs and outputs, retention practices, subprocessors, and rights to reuse customer data. Agreements that clearly separate operational service use from secondary AI training or analytics activities tend to create less negotiation friction. Terms that permit broad or undefined reuse of customer data are more likely to trigger escalation during procurement and legal review.

AI/data rights clauses frequently affect governance, confidentiality, vendor risk, and future operational exposure. Procurement teams often escalate agreements when data usage language appears broader than necessary for the service being purchased or when model training rights are difficult to interpret. Escalation is also common when agreements lack clear limitations on retention, third-party sharing, or output handling. In enterprise environments, unclear AI governance terms can create downstream compliance and trust concerns that require legal, privacy, or security involvement before approval.

Agreements often appear out of market when they request rights that exceed what enterprise buyers typically accept for comparable AI services. Examples include unrestricted model training on customer data, broad ownership claims over generated outputs, weak confidentiality protections, or the ability to materially modify service behavior without notice. Buyers also view agreements skeptically when liability structures remain heavily vendor-favorable despite elevated AI-related operational risks. Market-aligned agreements usually define AI usage boundaries clearly and avoid creating ambiguity around customer control, governance, or long-term data exposure.