How legal leaders can drive compliant AI adoption through privacy-by-design and intelligent data governance

The rapid proliferation of artificial intelligence (AI) is fundamentally reshaping the enterprise. From customer service automation to predictive risk modelling, AI is increasingly embedded into core business functions, often in highly regulated and risk-sensitive domains.

For legal leaders, this presents a dual challenge: to enable innovation while ensuring rigorous compliance with privacy regulations that continue to evolve across jurisdictions.

Download the checklist: Legal essentials for privacy-ready AI

The cost of getting it wrong is significant. Under the General Data Protection Regulation (GDPR), non-compliance can result in fines of up to 4% of global annual turnover. Beyond penalties, organisations face mounting risks of reputational damage, shareholder litigation, and regulatory scrutiny.

How, then, can Chief Legal Officers (CLOs) and General Counsel (GCs) proactively govern AI while enabling its strategic potential?

Privacy-by-design: a legal imperative for AI

Embedding privacy protections into AI workflows is a baseline expectation of modern data governance. Through 2026, 60% of AI projects will fail to deliver on business SLAs and be abandoned due to lack of AI-ready data and poor governance practices.

Regulatory frameworks such as the GDPR, the California Privacy Rights Act (CPRA), Virginia’s Consumer Data Protection Act (CDPA), and Colorado’s Privacy Act (CPA) now mandate proactive compliance measures including algorithmic impact assessments, data subject rights enablement, and robust consent management.

AI complicates this further by introducing new variables: inferred data, opaque decision-making (so-called “black box” models), and increasing reliance on unstructured content such as contracts, emails, and chat transcripts.

According to Gartner, “responsible governance of the data will always be required, but for AI, data governance principles may change,” particularly when dealing with diverse and dynamic use cases.

The legal risks of unprepared AI data

Despite the urgency, most enterprises are not yet ready.

A recent study by the BPI Network and EncompaaS found that 60% of business leaders lack confidence in their organisation’s data-AI readiness, with 30% of GenAI projects expected to fail post-proof of concept due to poor data quality, inadequate risk controls, or escalating compliance costs according to Gartner.

This is compounded by structural challenges:

  • Over 70% of enterprise data is unstructured, locked in file shares, email servers, legacy systems, and cloud repositories.
  • Two-thirds of organisations lack the data management practices required for AI, according to Gartner’s 2024 survey.
  • Data classification and enrichment remain incomplete, undermining the accuracy and reliability of AI outputs.

When GenAI is trained on inconsistent, siloed or sensitive content without appropriate oversight, organisations risk introducing bias, violating consent, or exposing regulated data, issues that can cascade quickly into legal exposure.

A proactive roadmap for privacy-ready AI

To navigate this environment, legal executives must lead the charge on operationalising privacy-by-design across AI initiatives. That starts with cross-functional collaboration and the right enabling technologies.

1. Establish a unified data inventory

Accurate inventories are the foundation of privacy-ready AI. Start by identifying all data sources across your organisation—including file shares, email archives, document repositories, cloud platforms, and legacy systems. This is especially important given that up to 90% of enterprise data is unstructured and often hidden in places like SharePoint drives and legacy systems (BPI Network).

Unstructured content frequently contains sensitive information that isn’t immediately visible or well-governed. Use discovery tools that can discover and classify data at scale, flag high-risk content (such as PII or contractual data), and automatically apply metadata. Prioritise visibility over completeness in the first phase. Aim for broad coverage, then iteratively improve depth and accuracy.

2. Automate classification and policy enforcement

Manual privacy controls cannot keep pace with the volume and velocity of AI pipelines. Legal leaders should advocate for automation in:

  • Metadata tagging
  • Sensitivity labelling (e.g. PII, PHI, financial data)
  • Policy-based retention and access control
  • DSAR and consent workflow integration

3. Incorporate algorithmic governance early

From impact assessments to model lineage and output validation, legal oversight must be embedded from the start, not applied after deployment. AI-ready governance includes:

  • Version control of training data
  • Documentation of derivation and inference chains
  • Observability metrics and data drift monitoring

4. Enable explainability and auditability

With regulators increasingly focused on the explainability of AI decisions, particularly where rights are affected, legal teams must ensure that models are both lawful and defensible. That means full transparency into what data was used, how it was processed, and how outputs were generated.

How EncompaaS supports privacy-ready AI

The EncompaaS platform uses AI to discover, enrich, organise and de-risk enterprise data, turning content chaos into AI-ready data.

By creating a normalised foundation of high-quality data, EncompaaS enables legal, risk and compliance teams to:

  • Gain full visibility over sensitive and regulated content
  • Automate governance policies at scale
  • Prepare unstructured data for responsible GenAI use
  • Accelerate AI initiatives safely, with confidence

AI-ready data is determined by the ability to prove the fitness of data for AI use cases, including demonstrating that it has been appropriately governed.

EncompaaS delivers this proof, consistently and at scale.

Final word: legal leadership is the differentiator

AI adoption is inevitable. The question is whether it will be secure, compliant, and trusted, or exposed, uncontrolled, and vulnerable.

For Chief Legal Officers and General Counsel, this is a defining moment. The legal function is uniquely positioned to lead AI governance, ensuring innovation doesn’t come at the cost of compliance.

The organisations that act now will not only safeguard themselves against regulatory fallout, but also unlock the full business value of GenAI with confidence.

Download the checklist: Legal essentials for privacy-ready AI

Get the practical steps every CLO and GC needs to drive compliant AI adoption, starting today.