Transvault: Enabling Compliance in the Age of AI 

In an era where artificial intelligence (AI) is rapidly transforming how organisations process data, interact with customers, and manage risk, compliance has become a more pressing concern than ever. The announcement by the Medicines and Healthcare products Regulatory Agency (MHRA) that an “overarching framework” for AI regulation is expected by mid-2026 underscores the urgency for enterprises to ensure regulatory and governance readiness.1  

Against this backdrop, Transvault emerges not just as a migration tool for legacy email archives, but as a strategic enabler of data integrity, auditability and defensible compliance; critical foundations for organisations adopting AI, data analytics, and digital transformation. 

According to the report from Digital Health, the MHRA’s chief executive pointed out that current regulatory regimes will struggle to keep pace with the speed of AI innovation. The framework being developed by the UK’s National Commission on the Regulation of AI in Healthcare aims to provide “clearer, simpler and more consistent processes that give industry confidence while ensuring every local system has autonomy”.  

Key take-aways for businesses: 

• AI is no longer optional; it is being embedded into core business functions, systems, and decision-making. 

• Regulators are moving from ad-hoc guidance to high-level overarching frameworks, meaning organisations must prepare for higher scrutiny, documentation, audit trails and governance.  

• Data integrity, traceability, chain-of-custody, and robust eDiscovery & archive capabilities will become central to regulatory compliance. 

• Industries such as healthcare, finance, legal and others are under pressure where data flows, decisions and AI models must be defensible, auditable and transparent. 

In short, companies that treat data governance, migration and archive as an after-thought will likely struggle when AI becomes subject to stricter regulatory oversight. 

Whilst Transvault is widely known for email archive and cloud-migration services, we are stepping into the world of AI governance with our Intelligent Ally. Our underlying value proposition aligns very closely with the demands of AI-ready compliance. Below are some of the core capabilities and how we map to regulatory needs. 

1. Intelligent Ally 

For regulated industries that maintain journal archives (e.g., email journaling in financial services, legal correspondence in law firms, or audit logs in healthcare), Transvault provides Intelligent Ally. It records prompts and responses for the most popular generative AI chatbots (ChatGPT, Copilot, Google Gemini, Claude)  so that data is preserved and discoverable, meaning there is a track record of AI use; a key requirement for eDiscovery and regulatory compliance. 

2. Auditability

Transvault offers “secure-by-design” architecture, running email migrations within the client’s security boundary, encrypting data in-flight, and supporting hash validation between source and target systems. The concept of chain-of-custody in email migration and archives ensures that data cannot be shown to have been altered, lost or tampered with. That gives organisations a defensible record of data movements, which is critical for litigation and regulatory inspections. Intelligent Ally will apply the same level of detail forAI-audit demands using our wealth of experience with chain-of-custody reporting. 

3. Integrity Checks & Reporting 

Transvault Migrator performs baseline checks: ensuring migrated items can still be searched, opened and validated in the target system. There’s also detailed audit reporting/mapping source IDs to target IDs, logging failures, triaging items that fail, and providing remediation services. For organisations deploying AI, we are applying this logic to Intelligent Ally so that organisations have provenance on data (where it came from, how it was transformed, where it now lives) as it is essential. 

4. Large-Scale, High‐Volume Support 

Transvault’s platform is built for enterprise scale: it supports many legacy platforms, large data volumes, and complex migrations across environments (on-prem, cloud, hybrid). Organisations adopting AI often face huge data volumes, and the backbone of their compliance posture depends on knowing where data resides, how it is tracked, archived and accessible for governance. 

5. Focus on Outcomes & Governance 

For organisations navigating AI regulatory readiness, this means working with a partner that doesn’t just do the “lift and shift” but ensures governance, visibility and auditability are baked in. 

In healthcare, AI is increasingly used for diagnostics, patient-monitoring, workflow optimisation and administrative tasks. But these use-cases involve sensitive personal health data, high regulatory scrutiny, and evolving frameworks such as the MHRA’s upcoming AI regulation. Organisations must demonstrate data integrity, provenance, audit trails and secure access. 

• Migrate legacy health-record archives, email logs, correspondence or audit trails into modern, compliant platforms with chain-of-custody and metadata preservation. 

• Provide audit logs and reporting so that when AI systems utilise archived data (for model training, inference or monitoring) the underlying data lineage is traceable. 

• Support retention and deletion policies in line with healthcare-specific regulations (e.g., medical device software records, clinical correspondence) to ensure compliance even before AI is layered on. 

Given the increasing regulatory focus on AI (see the MHRA timeline to mid-2026) and the need for data governance to underpin AI models, organisations should consider the following steps: 

1. Audit your legacy data footprint: Map all sources of archived data (email, journals, PSTs, legacy systems) and assess the compliance risks (e.g., broken archives, inaccessible items, unsupported formats). 
2. Define a migration & archive strategy: Using a solution like Transvault, plan migration of legacy archives into modern platforms with full audit logs, chain-of-custody, integrity checks and metadata preservation. 
3. Align archive/retention governance with AI demands: Define how archived data may be used by AI (training, inference, analytics), ensure retention/deletion policies reflect this, and ensure the provenance of that data is recorded. 
4. Embed auditability and monitoring: Ensure migration logs, integrity checks and audit reports are retained and can feed into governance frameworks (AI model validation, regulatory inspection readiness). 
5. Prepare for AI-regulation: With the MHRA signalling an overarching AI framework by 2026, organisations should ensure their data foundations (archives, migration trails, audit logs) are ready to support model explainability, impact assessments, and traceability of AI decisions. 
6. Ongoing governance: After migration, ensure your archive environment remains managed (updates, retention reviews, compliance checks) so that any AI built on top of that data remains defensible. 

 As AI becomes increasingly embedded in business processes, it is no longer sufficient to apply compliance only to the AI layer. The foundation i.e. the data, archives, migrations, audit trails and governance mechanisms must be robust and defensible. With regulators such as the MHRA signalling that a high-level AI regulatory framework is imminent, organisations across all industries must act now. 

Transvault offers a strong bridge between legacy data infrastructure and modern, compliant data platforms. By ensuring data integrity, traceability, auditability and governance readiness, Transvault helps businesses not only migrate their archives, but prepare their data foundations for the next wave of AI regulation. With that in place, firms can pursue AI innovation with greater confidence that their data backbone will withstand regulatory scrutiny. 

Author – Kate Coventy, Senior Product Owner

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