From Black Box to Contract Clause: Governing AI in Outsourcing Deals

Artificial intelligence now runs through almost every sizeable technology and outsourcing deal, whether as a core deliverable or embedded in “background” tooling. In that landscape, outsourcing and technology contracts need to move beyond generic boilerplate and address AI‑specific risk, governance and regulation in a structured and operationally practical way. This article sets out a pragmatic framework to position AI within the deal, align obligations with evolving regulatory regimes, and translate “responsible AI” principles into enforceable, auditable contract terms.

Why does it matter?

AI is no longer peripheral. It shapes regulatory exposure, decision quality, customer outcomes, security posture and supplier economics. Contracts that fail to grapple with AI explicitly risk unmanaged bias, opaque failure modes, compliance gaps and costly renegotiation when regulators, auditors or incidents demand changes.

Positioning AI in the deal
A robust contract starts by accurately describing what AI is in scope and how it is used within the outsourced services. Precision on scope underpins governance, compliance allocation, intellectual property rights and performance management.

• Define the AI components clearly
  Distinguish between customer‑facing AI (for example chatbots, decisioning engines, recommender systems), internal toolsets (for example code‑assist, monitoring and anomaly detection tools) and third‑party AI services consumed via APIs.
  
  Clarify whether AI is embedded in deliverables, used only for provider internal efficiency, or both. Where internal tools influence outputs consumed by the customer, the contract should still treat the tools as within the scope of AI governance.
• Classify AI by criticality and use case
  AI that affects regulatory obligations, safety, employment, credit decisions, healthcare triage or other protected domains should be identified as “high‑risk” or equivalent, with enhanced controls aligned to emerging regimes such as the EU AI Act, sector guidance and relevant common‑law duties. Lower‑risk automations can be governed proportionately, but the classification should be reasoned, documented and revisited as models or use cases evolve.

Contractually, this is typically achieved through a combination of scoped definitions (for example AI Functionality, AI System, Model), a technical annex describing the models, training approach and deployment architecture, and an AI use‑case register that can be updated during the term under a structured change mechanism. The technical annex should indicate whether models are foundation, licensed third‑party, open source, bespoke, or fine‑tuned; describe data sources and preprocessing; and outline deployment patterns, including guardrails, monitoring and fallback

Governance, transparency and explainability
AI‑enabled outsourcing requires more intensive governance than traditional “lift and shift” arrangements because models evolve, are data‑dependent and may be opaque in operation. Governance must be layered, with clear accountability, timely reporting and enforceable escalation.

• Establish governance structures 
  The contract should mandate an AI‑specific governance layer - typically an AI Working Group reporting into the main governance board - with authority over deployment approvals, risk assessments, model changes, exceptions and remediation plans. Membership should include responsible AI, risk/compliance, security, data protection and business owners, with the provider’s technical lead accountable for model performance and lifecycle.
• Mandate Transparency and explainability 
  Customers increasingly require disclosure of the types of AI used, logic and features of high‑impact models, training and validation artefacts, performance metrics, material model changes and ablation or sensitivity testing results. Where outputs affect individuals or regulatory obligations, disclosures should support regulatory filings, model cards or equivalent artefacts, and audit trails.
  

Where “black box” models are used, the contract should require either meaningful explanations of outputs, or compensating controls such as human review, conservative thresholds, additional testing, and outcome‑monitoring for drift and bias. The parties should document the explainability level required by use case, recognising that statistical explanations, feature importance and outcome‑focused testing can be acceptable where model internals are proprietary or opaque.

Data, training and intellectual property 

Data and intellectual property are central to AI value and risk. Outsourcing contracts need a more granular data and IP regime when AI is involved, including restrictions on training, clarity on outputs, and indemnities calibrated to model provenance.

• Control the use of customer data for training 
  The contract should spell out whether the provider may use customer data- including personal data, confidential information and logs - to train or fine‑tune models, whether on a segregated basis or into shared/foundation models, and on what terms. Those terms may include opt‑out rights, anonymisation and aggregation standards, data minimisation, differential privacy or other privacy‑enhancing techniques, and security controls. A default position of “no training without express written consent” is often appropriate for regulated or sensitive data.
• Clarify ownership and rights in models and outputs
  The parties need to decide who owns pre‑existing models, fine‑tuned or customised models developed for the customer, and AI‑generated outputs such as code, documents or analytics. Where the Provider uses multi‑tenant platforms, licences and usage restrictions should protect both parties: the customer should receive sufficient licence rights to use outputs and any customer‑specific model artefacts, while the provider retains rights in background IP and generalised learnings that do not disclose customer confidential information.
• Allocate third-party IP risks
  The contract should address third‑party IP claims arising from training data, model artefacts or outputs, including indemnities, remedial obligations and cooperation. Indemnity scope may vary by provenance: broader cover is often expected where the provider supplies the model and training corpus; more limited cover may be acceptable where the customer mandates specific training data or models.
• Open source and model lineage
  Open‑weight and open‑source model usage requires lineage and licence tracking. Contracts should require the provider to identify relevant licences, comply with attribution and notice obligations, and avoid incorporating copyleft terms that could contaminate customer deliverables. 

Regulatory and ethical compliance 

The regulatory overlay for AI is quickly hardening, with a mix of horizontal AI regulation, sector‑specific rules and re‑purposed regimes such as data protection, consumer protection and equalities law. Contracts must allocate compliance responsibilities, embed policy adherence and support auditability.

• Allocate compliance responsibilities 
  The contract should allocate responsibility for identifying, monitoring and complying with applicable AI‑related law and guidance, including AI‑specific regimes (for example the EU AI Act), data protection, employment rules, sector conduct standards and equalities legislation. Providers should warrant compliance for AI they supply and control; customers should take responsibility for business rules and decisions beyond the provider’s control. Joint responsibilities should be clearly delineated, with cooperation and cost principles.
• Embed policies and standards 
  Customers increasingly impose Responsible AI or Acceptable AI Use policies on their supply chains. Contracts should require providers and their sub‑processors to comply with those policies, notify material non‑compliance, and support impact assessments and audits. For high‑risk use cases—such as recruitment, credit risk scoring and healthcare—contracts should require systematic risk assessment, testing for bias and discrimination, documentation of mitigation measures, and human oversight consistent with regulatory expectations. 

Performance, SLAs and service evolution 

Traditional SLAs built around uptime and response times are insufficient for AI‑enabled services. Quality‑of‑outcome metrics, lifecycle controls and change frameworks are required to manage model drift, bias, and evolving regulatory expectations.

• Define AI‑specific performance metrics
  Service levels may include accuracy rates, precision/recall, false positive/negative thresholds, latency for model inference, coverage rates, calibration error, and acceptable ranges for key process KPIs. Targets should be grounded in realistic baselines, with measurement methodology, sample sizes, test data representativeness and confidence intervals agreed to avoid disputes.
• Manage continuous improvement and lifecycle
  Contracts should address training frequency, triggers for retraining or rollback, monitoring thresholds, and the customer’s right to require adjustments if outputs cease to meet standards or regulatory expectations. A/B testing, pilot stages and controlled sandboxes should be formalised with entry/exit criteria, guardrails and rollback rights. Changes to models that impact accuracy, fairness, explainability or regulated outcomes should be treated as “material changes” requiring approval.
• Measuring performance fairly
  Agree the test methodology. Disputes often arise from mismatched datasets or definitions. Lock down gold‑standard datasets, sampling, acceptability thresholds, seasonality handling and re‑baselining cadence in the SLA schedule.
  

Liability, risk allocation and insurance

AI introduces new liability vectors and amplifies existing ones, from inaccurate outputs to security breaches and regulatory enforcement. Risk allocation should reflect model provenance, control, and use case severity.

• Address liability for AI outputs
  Contracts should expressly address liability for incorrect, incomplete or biased AI outputs, including where the customer has reasonably relied on them in making decisions. Consider carve‑outs for specific harms (for example unlawful discrimination) where the provider’s model or configuration is the proximate cause. Where customers alter thresholds or override guardrails, a contributory fault regime should apply.
• Consider carve‑outs and risk‑sharing
  The parties may consider carve‑outs from liability caps for certain AI risks, such as data protection fines attributable to provider failures or third‑party IP claims. Risk‑sharing mechanisms—gain‑share and pain‑share—can be linked to AI‑driven automation and cost savings, aligning incentives for performance improvement and error remediation.
• Calibrate insurance
  Given the evolving risk landscape, require insurance coverage that expressly responds to AI‑related claims, including technology E&O, cyber and media liability. Ask for evidence of coverage, exclusions review, and cooperation around notice and claims handling where AI is implicated.

Security, robustness and resilience

AI systems introduce new attack surfaces and failure modes, including prompt injection, data poisoning, model inversion and adversarial examples. Contracts should impose AI‑specific security controls and robust operational resilience.

• Mandate technical and organisational measures
  Contracts should require secure development practices for models, data pipeline security, access control and segregation, encryption at rest and in transit, monitoring of inputs/outputs, anomaly detection and defences against adversarial attacks. Safeguards for LLMs should include input/output filtering, prompt injection hardening, toxic content controls, rate limiting and isolation for high‑risk contexts.
• Plan for operational resilience
  For business‑critical AI, address fallback and continuity arrangements: degraded non‑AI processes, manual workarounds, or alternative models and providers, with defined recovery time and recovery point objectives. Regulated financial services and critical infrastructure customers should dovetail AI resilience obligations with existing operational resilience and outsourcing frameworks, including substitutability and severe but plausible scenario testing.

Third‑party supply chain and subcontracting

Many providers rely on third‑party AI platforms or models, raising questions about back‑to‑back protections, transparency and accountability across the supply chain.

• Require visibility and approval
  Contracts should require disclosure of key AI‑related subcontractors, cloud providers and model suppliers, with approval rights, minimum due diligence standards and notification obligations for changes that could materially impact risk. The disclosure should include geographic location, data residency and the nature of access to customer data.
• Impose back‑to‑back obligations
  Where the provider leverages hyperscalers or foundation model vendors, the customer will want the provider to flow down key obligations—security, compliance, audit, data use restrictions—and to stand behind performance and compliance of those components, rather than pushing risk directly onto the customer. Commercially, the provider should manage alignment between upstream licence terms and downstream commitments.
• Address cross‑border issues and audits
  Supply‑chain provisions should deal with data localisation, cross‑border transfers and access to training data and logs for audit or regulatory investigations, consistent with data protection and sector‑specific requirements. Contracts should facilitate regulatory access where mandated, with appropriate confidentiality protections.

Human oversight, workforce and change management

Deploying AI in outsourced services has significant human and organisational implications, within the provider’s delivery teams and for the customer’s retained organisation.

• Implement human‑in‑the‑loop where appropriate
  For higher‑risk AI use cases, regulators and best practice recommend or require meaningful human oversight. Contracts should define when human review is mandatory, the qualifications and authority of reviewers, and sampling and audit frequencies. Thresholds for automatic decisions should be justified and documented; contested decisions require clear escalation.
• Anticipate workforce impacts
  Deals that rely heavily on automation and AI may trigger employment law, consultation and TUPE‑style issues, as well as obligations to consult with staff bodies or unions. These should be anticipated and allocated at bid stage, with transition plans, knowledge transfer and upskilling commitments. Customer‑facing communications should clearly explain where and how AI is used, in line with transparency obligations.
• Human oversight in practice
  Oversight is more than a rubber stamp. Define decision rights, provide tooling for effective review, capture rationale, and audit outcomes to detect drift or unintended discrimination. Where throughput pressures exist, specify minimum time‑per‑case or caseload caps.

Dispute resolution, audit and exit

AI calls for more deliberate audit rights, dispute resolution mechanisms and exit strategies, given the complexity of disentangling AI‑enabled services and the sensitivity of model artefacts.

• Strengthen audit and oversight rights
  Customers should have the right to audit AI systems and processes directly or via independent experts for compliance with law, security, performance and ethical requirements. Access should include relevant logs, documentation and testing artefacts, with confidentiality carve‑outs and clean‑room procedures to protect provider IP and third‑party licences.
• Plan for exit, transition and model portability
  Contracts should consider how AI‑enabled services will be unwound or transitioned at exit, addressing access to trained models, weights, configuration, training data and documentation, subject to third‑party restrictions and law. Where portability is constrained, require interoperable data exports, assistance in recreating customer‑specific fine‑tuning, and run‑off support to avoid value destruction.
• Adopt tailored dispute mechanisms
  Disagreements over bias mitigation, explainability or regulatory interpretations may benefit from early expert determination or technical mediation rather than immediate litigation. Define a fast‑track escalation path for material AI incidents with agreed technical experts.

Operating model: putting it all together

A contract is only effective if the operating model supports it. To embed these provisions:

• Document artefacts
  Require model cards or equivalent documentation for each AI use case; maintain a risk register and impact assessments; and align service reviews to AI performance and incident trends. Tie payment mechanisms to outcome metrics where feasible, with clawbacks or service credits for sustained underperformance.
• Integrate processes
  Build AI testing and validation into release management; align change control with model lifecycle; and require incident management to include bias, drift and adversarial vectors alongside traditional security incidents. Ensure privacy, security and responsible AI review gates are explicit in the delivery lifecycle.
• Enable agility
  Use sandbox clauses and controlled pilots to introduce new capabilities safely. Provide for regulatory change adjustments without renegotiating the whole contract, using predefined governance triggers and cost principles.

Conclusion

Moving from black box to contract clause requires specificity. Define scope and risk, align governance and transparency, regulate training and IP, allocate compliance and liability proportionately, and codify lifecycle, security and resilience for AI. Build the operating model around these commitments and preserve portability at exit. With these elements, outsourcing agreements can unlock the benefits of AI while managing the evolving risk and regulatory landscape.

This article is for general guidance only and does not constitute legal advice on which the recipient can rely. Specific legal advice should be obtained in all cases.