Best practices for managing third-party AI risk

Implementing best practices for managing third-party AI risk starts with a fundamental shift: moving from static contract reviews to technical, real-time enforcement. As AI reshapes every category of enterprise software, organizations need precise answers about which vendors use AI, what models they run, how those models handle data, and what consent policies apply.

This guide offers a roadmap for controlling vendor AI usage through code-level guardrails, automated permissions, and integrated compliance infrastructure that scales across complex vendor ecosystems.

The expanding landscape of third-party AI providers

AI is reshaping SaaS businesses in a dramatic way. Your marketing automation platform offers predictive lead scoring, your CRM delivers sentiment analysis, and your analytics stack uses machine learning for anomaly detection. Each feature represents a new data pathway you likely can't fully control.

The challenge isn't just knowing which tools teams use—you need precise visibility into how and where AI capabilities operate. AI is now present in nearly every tool teams adopt, but most organizations lack clear sight lines into vendor data handling.

Connectivity emerges as the chief bottleneck. AI systems aggregate information from warehouses, CRMs, event streams, and unstructured data stores. Some systems perform actions as users, making it difficult to separate human and machine access. When permissions remain static and AI usage goes unseen, compliance weakens and data risk escalates.

Financial services leaders have recognized this challenge and are investing 74% more in data management and infrastructure through 2025 compared to other industries, understanding that only governance-first approaches scale safely.

Identifying key risks in AI vendor partnerships

Legacy third-party risk management is no longer enough to meet the complexity and speed of AI. Classic models focus on static contract review and periodic audits, but AI brings dynamic, quickly evolving risks that most compliance teams simply can't track manually.

The primary vulnerabilities:

• Lack of visibility into data use: Without real-time insight into vendor processing, regulatory compliance is inherently incomplete. Frameworks like the EU AI Act and NIST's AI Risk Management Framework emphasize demonstrating knowledge of vendor data handling. 92% of AI contracts claim data usage rights beyond what's strictly necessary, versus 63% for standard SaaS deals.
• Fragmented permissions: Many large enterprises hold years of disjointed consent and preference data across dozens or hundreds of systems. Siloed permissions prevent downstream systems from accurately distinguishing between data users have permitted for training and data they've opted out of.
• Ungoverned training and usage: Most organizations can't reuse data for new AI initiatives without seeking additional consent or establishing a legal basis. 97% of companies with AI-induced data breaches lack formal governance structures.
• Integration and access risk: AI draws data from numerous sources. Static permissions can't keep up. You need clear, purpose-driven, real-time enforcement across all flows, with visibility into who accessed what, when, which model used it, and whether users gave permission.

If privacy and data governance workflows remain manual and ticket-based, exposure time increases and customer trust erodes. Each missed deletion, outdated permission, or overlooked opt-out creates gaps regulators, customers, and your board will detect.

Best practices for managing third-party AI risk

The most effective approach starts with building technical guardrails, not just relying on legal language. Modern AI pipelines accelerate data movement and transformation, outpacing what manual risk management can control.

Use code-level governance as your foundation

By implementing guardrails at the application and infrastructure layers, you observe, audit, manage, and limit data flows between your organization and every AI system. Technical safeguards extend beyond policy, operationalizing risk reduction at the code-level.

Written data usage policies don't suffice. Implement technical controls over all interactions between your environment and LLMs using a middleware or proxy layer. This approach supports both auditability and scalable policy implementation.

Strengthen risk reduction through network and encryption controls

Transcend's Sombra™ gateway employs a reverse tunneling architecture to connect the Transcend Cloud with your private network or VPC, reducing internet exposure and overall attack surface.

Sombra™ also conducts Diffie-Hellman key exchanges to generate shared cryptographic keys, ensuring all communications are end-to-end encrypted. With pre-entry encryption at the HTTP API layer, plaintext never leaves your perimeter, which is essential when sending data to external AI systems.

Structuring vendor oversight and accountability

It's important to base governance on systematic risk assessment. Use frameworks like NIST AI RMF to structure programmatic review of deployed and planned AI systems. Integrate ethics, privacy, and security into every stage of the AI system lifecycle.

Comprehensive risk management requires:

• Identifying and assessing risks associated with each AI use case
• Implementing controls to mitigate these risks
• Ongoing monitoring and evaluation of control effectiveness

Vendor contracts should include AI-specific requirements beyond uptime: model accuracy, drift thresholds, explainability metrics, trigger events for retraining, bias auditing with fairness metrics, transparency obligations, and clear liability structures for AI-induced outcomes.

Transcend Vendor AI Usage offers automated detection of vendor AI features, models, and policies. This contextual mapping flags systems likely to contain sensitive data and empowers granular review across your vendor landscape. The product uses an LLM-based scanning engine to maintain an up-to-date inventory of your organization's AI footprint.

Connect vendor AI findings to compliance reports like your record of processing activities (ROPA) and related risk assessments. This approach enables governance, privacy, and risk leaders to create more complete assessments, maintain compliance documentation, and make smarter procurement and data-sharing decisions.

Enforcing real-time permissions and compliance

Legacy, static permissions can't match AI's data flow velocity. You need clear, flexible, real-time enforcement at every step.

Purpose limitation stands as a cornerstone of most privacy laws. Data collected for one use can't be legally reused for another without securing new consent or a legal basis. For AI, user consent must be specific and explicit.

Transcend captures consumer permissions for AI training in real time. Automated syncing keeps these permissions consistent across all user data, ensuring your ML teams have authoritative consent signals for every training dataset.

Transcend facilitates detailed, synchronized permissions, guaranteeing opt-outs or restrictions follow user data everywhere—across warehouses, training platforms, or live models.

Many enterprises still deal with permissions scattered across multiple systems and slow, ticketed access requests. Real-time data permissioning delivers a centralized compliance layer, automatically enforcing user rights before data feeds into downstream AI consumers. This substantially reduces unauthorized usage risk.

Building an automated compliance layer with Transcend

You can't apply permissions without knowing what's present in your ecosystem. Real-time classification and discovery replace slow, manual inventory approaches—they're non-negotiable for large enterprises meeting requirements under the EU AI Act and similar standards.

Transcend offers automated data discovery and categorization for personal data across structured, semi-structured, and unstructured storage. Its classification suite continuously monitors your data estate and highlights sensitive attributes to preempt noncompliant use by third-party AI.

For permissions enforcement, Transcend Preference Management centralizes collection, storage, and application of user choices. These workflows propagate opt-outs and other settings consistently from analytics platforms to live inference models.

Transcend offers Do Not Train and deep deletion features. Enterprises can formally exclude user data from AI training or erase content from models on receipt of erasure requests, including from caches and backups.

As user consent requirements tighten for AI training, leading companies are adopting sophisticated preference management engines. Transcend handles automated updates and synchronization, keeping consent current across your ecosystem.

Leveraging Transcend's integrations for streamlined data flows

Custom scripts and siloed connectors don't scale. The solution is a unified data compliance layer: a central platform to normalize and apply permissions universally. This clarity reduces integration overhead—critical for smooth AI deployment.

Transcend's purpose-built integrations enable real-time propagation of consent choices across legacy and modern systems. With more than 220 connectors, Transcend outpaces standard consent management options.

With integrations in place, Consent Workflows automatically trigger updates across data lakes, AI pipelines, and production, removing the need for custom scripts. A single compliance layer absorbs vendor or infrastructure changes with minimal disruption.

Reducing engineering complexity

Current governance practice often relies on manual oversight and ad hoc code, draining valuable engineering resources. AI-powered data governance can reduce onboarding times by 40–50%.

Automated syncing, system-level enforcement, and turnkey integrations mean Transcend customers eliminate repetitive engineering tasks and redeploy teams to higher-value initiatives like platform modernization and new AI services. Policy changes propagate instantly, onboarding new systems happens in a single workflow, and protections scale as models evolve.

Transcend empowers organizations to activate AI responsibly and at enterprise scale. Preference changes sync everywhere, policies update systemwide, and permissions are enforced from day one for any new model or tool.

Key considerations for continuous AI risk mitigation

AI risk management isn't a one-time task. Continuous auditing and event logging are fundamental for transparency and accountability. They validate who accessed which data, when, and for what reason.

Connect monitoring directly into the stack so you can flag unauthorized use immediately. Live dashboards should map each model to its datasets, indicating whether every use aligns with current user consent. Regular reviews and updates ensure accurate permissions as your systems grow and evolve.

Transcend Vendor AI Usage gives shared, real-time visibility across privacy, security, and procurement teams, ensuring everyone works from an up-to-date view of enterprise AI risk.

Ongoing monitoring and metrics

Robust programs include:

• Ongoing model drift and performance monitoring
• Data quality surveillance, like watching for missing values and schema changes
• Bias detection and remediation in production outputs
• Security anomaly detection, like adversarial or prompt injection attacks
• Dependency tracing for upstream data and service API failures

Recommended audit cycles are quarterly for acceptable use and handling standards, semi-annual for risk validations, plus real-time alerting for priority issues. Frequent governance reviews help maintain both maturity and continuous improvement.

Transcend provides comprehensive logs, lineage, and enforcement over user-data, delivering provable end-to-end compliance. This empowers CIOs to confidently lead new AI growth, cross-entity data sharing, and digital expansion without hidden risks.

Proactive regulatory alignment

The EU AI Act sets stringent requirements for high-risk systems: 10-year retention and documentation, mandatory conformity assessments, CE marking, and post-market monitoring. Most enterprises struggle to document data flows across complex technical estates, exposing themselves as they scale AI-driven products.

Transcend automates these updates, offering a governance layer that future-proofs compliance as policies shift worldwide—providing auditability and risk management features that enable organizations to scale responsible AI while maintaining regulatory assurance.

Charting a secure AI roadmap: Best practices for managing third-party AI risk

The right approach to third-party AI risk delivers proactive, real-time governance. Rather than relying only on contracts, adopt tools and workflows that deliver continuous, permission-aware oversight of every data use and AI training scenario.

Integrate technical guardrails at the code and infrastructure layers. Build a single, automated compliance platform to control all data flows, and enforce encryption, reverse-tunneling, and end-to-end protections—especially for sensitive or regulated data.

Operate from a unified compliance layer so that every system, dataset, and model always honors up-to-date user permissions. This ensures only fully consented data enters any AI workflow, including those managed by third-party vendors.

Transcend delivers the visibility, automation, and technical controls necessary to manage vendor risk as AI adoption accelerates. Contact us to learn how Transcend can help your organization drive AI innovation within a governance program that scales.