Mergers and Acquisitions: How to prevent AI from blending the information assets of two companies

Mergers and acquisitions (M&A) raise data-sovereignty concerns when artificial intelligence (AI) is used to analyse, classify or migrate information assets. Mixing knowledge bases, models or metadata can compromise confidentiality, compliance and the strategic value of both companies. The challenge is to isolate their information assets while using AI’s analytical capabilities within the applicable legal framework.

Introduction: When data becomes the central stake of a merger

Imagine a large cloud services company (the acquirer) decides to integrate a start-up specialising in natural language processing. The first step often consists of 'feeding' existing AI models with the target's datasets to accelerate time-to-market. In an ideal scenario, each dataset remains clearly identified, usage rights are respected and no sensitive data leaves the authorised perimeter.

In reality, several factors tend to blur these boundaries:

• Volume and variety: petabytes spread across data lakes, SQL/NoSQL databases and distributed file systems.
• AI Automation: ingestion pipelines applying feature engineering, data augmentation or transfer learning without granular human control.
• Time pressure: technical teams strive to deliver Proofs of Concept (PoC) rapidly to justify the operation.

The result can be an unintentional mix where models trained on protected data (GDPR, trade secrets, personal health information...) are deployed in an environment that does not comply with legal or contractual requirements. This phenomenon, which we call data-blending risk, exposes the company to regulatory sanctions, loss of competitive advantage, and reputational damage.

Governance, technical architecture and operational processes must be coordinated from the outset to keep information assets separate while using artificial intelligence securely.

1 Risks of intermixing information assets

These risks manifest at different levels: storage (data lake vs data warehouse), processing (ETL/ELT pipeline), and operation (model deployment). An isolation framework must therefore cover the entire data lifecycle.

2 Applicable legal and regulatory framework

The key obligations to prevent intermixing are:

• Minimisation principle – collect and process only the data strictly necessary for the purpose of evaluation or integration.
• Security-by-Design – apply encryption, tokenisation, and network segmentation from the ingestion phase onwards.
• Traceability – log every access, transformation, and model training event to provide proof of compliance in the event of an audit.

3 Segregation Architecture Principles

3.1 Dedicated Data Zones (Data‑Zones Architecture)

+-------------------+      +--------------------+
|   Zone « Source » | ---> |   Zone « Sandbox » |
| (données brutes)  |      | (traitement IA)    |
+-------------------+      +--------------------+

+-------------------+      +--------------------+
|   Zone « Target » | <--- |   Zone « Vault »   |
| (cibles final‑)   |      | (stockage chiffré) |
+-------------------+      +--------------------+

• Source Zone – Contains raw datasets for each entity, stored in separate buckets with role-based access controls (RBAC).
• Sandbox Zone – An ephemeral environment where the AI performs profiling and feature generation without persisting results. Access is restricted to temporary service accounts.
• Vault Zone – Encrypted storage (AES‑256) with centralised key management (KMS), dedicated to artefacts that must be shared (e.g., aggregated models).

This architecture ensures that raw data never leaves its zone of origin, while only authorised derivatives are circulated.

3.2 Isolation via Containers and Virtual Networks

In practice, each AI pipeline is deployed within a dedicated namespace, with network policies that only permit communications to the Vault zone. Internal namespace traffic utilises mutual TLS provided by the service mesh, ensuring integrity and confidentiality.

4 Governance and Data Catalogues

4.1 Shared Data Catalogue with provenance metadata

A central catalogue (e.g., Apache Atlas, Collibra) must record:

• Provenance – Unique dataset identifier, owner, sensitivity level.
• Access Policies – Rules based on GDPR (e.g., right to be forgotten), contractual clauses and sovereignty requirements.
• AI Processing Status – Indicates whether the data has been 'ingested', 'transformed' or 'archived'.

The catalogue acts as a single source of truth, enabling DevSecOps teams to automate the creation of policies-as-code (e.g., Open Policy Agent – OPA) which are applied to every pipeline.

4.2 AI Model Management

Model artefacts must be treated as information assets:

Model-Ops relies on registries (e.g., MLflow, Seldon) that integrate compliance controls directly within the model registry.

5 Technical methods to prevent intermingling

5.1 Encryption and tokenisation

Tokenisation is particularly useful when a model requires access to PII columns: the actual data remains in the Source zone, while the pipeline only sees a non-reversible token.

5.2 Sandboxing and dynamic Data Masking

Sandbox environments (e.g., AWS SageMaker Studio Lab, Azure ML Compute) are provisioned with immutable images that include no access to production volumes. A data masking engine applies masking rules in real time (e.g., replacing names with pseudonyms) before the data is consumed by the AI.

5.3 Attribute-Based Access Control (ABAC)

Use a policy engine such as OPA to define rules such as:

package data.access

allow {
    input.user.role == "data_scientist"
    input.resource.sensitivity_level <= "confidential"
    input.action == "read"
}

These policies are synchronised with the metadata catalogue, ensuring that permissions evolve automatically based on the classification level.

5.4 DevSecOps Automation

The integration of IaC (Infrastructure as Code) with encryption and network modules ensures that each environment reproduces the same segmentation.

6 DevSecOps process for data migration

• Initial audit – Inventory information assets via the catalogue, classifying them by sensitivity.
• Definition of the Data‑Separation Policy – Document sharing rules (e.g., "models may only be trained on anonymised aggregates").
• Zone provisioning – Create S3 buckets, Azure Blob or GCS with server-side encryption and strict IAM policies.
• Sandbox pipeline deployment – Use a pipeline as code (e.g., Terraform + Helm) granted only read permissions for the source zone and write permissions for the vault.
• Controlled training – Launch training using masked datasets; record metrics in the MLflow registry and access logs in CloudTrail/Log Analytics.
• Legal validation – Ensure the DPO and legal department validate that generated artefacts comply with contractual clauses.
• Production promotion – Deploy the model via model serving with access exclusively to the vault, never to raw data.
• Post‑migration audit – Execute compliance queries (e.g., "Was there any direct access to customer data after 01/07/2024?") via centralised logs.

This automated and versioned workflow ensures traceability, reproducibility, and auditability of the migration process.

7 Realistic use case: acquisition of a fintech by a bank

Result: the bank was able to integrate the fintech's analytical capabilities while complying with GDPR and the data-locality requirements imposed by the Banque de France.

8 Key considerations and limitations

Key considerations

• ABAC policy complexity: an overload of rules may lead to authorisation errors.
• Cost of large-scale encryption: encryption throughput can become a bottleneck for massive pipelines.
• Biases induced by tokenisation: masking certain variables may impair the predictive quality of the models.
• Regulatory evolution: sovereignty requirements (e.g., EU Data Act) may change rapidly, necessitating continuous legal monitoring.

Technical limitations

• Homomorphic Encryption – While promising, it remains too slow for large-scale models and is unsuitable for real-time pipelines.
• Total isolation vs. integration needs – Over-segregating data can hinder the creation of cross-features essential to model performance. A compromise must be struck between security and the quality of insights.

9 Operational conclusion

Preventing an AI from blending information assets during a merger or acquisition is not solely a technical matter: it is a multidimensional challenge encompassing cybersecurity, data governance, regulatory compliance, and business optimisation. By adopting an architecture with clearly defined zones, automating governance via metadata-enriched catalogues, and integrating security controls into the DevSecOps pipeline, organisations can:

• Guarantee the sovereignty of sensitive data,
• Maintain compliance with legal requirements (GDPR, ISO 27701, etc.),
• Preserve the value of AI models by preventing contamination,
• Accelerate integration through reproducible and auditable processes.

Success depends on legal and business stakeholders validating each technical decision from the outset. This governance lets the teams use AI while preventing catastrophic mixing of the companies’ information assets.