By Praveen Ojha, Chief Technologist, EPAM Systems India Pvt. Ltd.
As GenAI (Generative AI) accelerates across enterprises, a second parallel ecosystem has quietly taken shape: Shadow AI. Much like shadow IT in the cloud era, these are AI tools, models and workflows adopted by employees outside of formal governance. Their intent is often efficiency, but their impact is disproportionately risky. What makes Shadow AI more complex than its predecessors is the speed of adoption, the invisibility of model interactions and the sensitivity of the data now flowing into external systems.
Recent research from MIT highlights this gap: employees at more than 90% of surveyed companies are using personal AI accounts for daily work tasks, while only 40% of organisations provide official LLM (large language model) tools. This data illustrates the core challenge: AI has become essential to productivity, but enterprise controls have not kept pace.
The Risk Lives at the Intersection of Data, Code and Compliance
Shadow AI creates exposure across multiple domains simultaneously.
- Customer and confidential data can be shared in prompts during content drafting, summarisation or reporting workflows.
- Proprietary code may inadvertently flow into public model endpoints, thereby weakening intellectual property boundaries.
- Compliance risk escalates when workflows touch regulated data or fall outside formal audit mechanisms.
With regulatory frameworks tightening and emerging national standards, unsanctioned AI activity can quickly become a governance liability. Instead of reactive controls, organisations are now moving toward multi-layered visibility frameworks: monitoring external AI calls, classifying enterprise assets by sensitivity and tracking unmanaged AI usage.
Forward-looking teams are even translating these metrics into financial exposure scores, linking AI misuse to operational, reputational and regulatory impact. Assigning monetary value to Shadow AI risk has proven effective for prioritising mitigation at leadership levels.
Why traditional security frameworks fall short
AI traffic doesn’t behave like traditional network or software activity. Model calls, third-party plugins, prompt flows and vector queries often bypass legacy monitoring tools. The result: organisations with strong cybersecurity practices may be blind to critical AI interactions.
To respond, enterprises are beginning to reinterpret zero-trust principles for the AI era, where every model invocation becomes a potential data boundary. Governance is shifting from static protection rules to adaptive oversight, capable of detecting anomalous model behaviour, unexpected data movement and unauthorised AI-assisted workflows.
A structured foundation is essential, comprised of trusted assessment frameworks, tested architectural blueprints and scalable AI operating models. Some organisations are pairing these with comprehensive training programs to build AI-literate leaders and teams, ensuring governance evolves alongside capability. This reflects a broader shift: responsible AI has now become the foundation of durable competitive advantage.
Innovation vs control: The new leadership balancing act
The instinctive organisational response to Shadow AI might be to restrict or ban external tools altogether. But a ban often fuels the very behaviour it seeks to eliminate. The more effective approach emerging globally is controlled enablement.
Leadership teams are increasingly:
– Establishing secure sandboxes where employees can experiment safely
– Creating internal AI marketplaces and copilot platforms tied to governed data sources
– Offering vetted alternatives to public AI tools to curb the need for “shadow” solutions
This strikes a necessary equilibrium: protecting the enterprise without stifling innovation. Policies that empower responsible use tend to reduce Shadow AI far more effectively than policies designed around prohibition.
Growing demand for demonstrable AI governance
A noticeable shift is underway worldwide. Regulators, global partners and enterprise clients are seeking evidence of formal AI governance models, not just intent. For example, as per the Digital India Act, sectoral data localisation rules and global regulatory momentum are prompting enterprises to strengthen AI auditability, model documentation and workforce training.
For many organisations, AI governance has moved from an operational task to a board-level agenda. Investment is flowing into responsible AI processes embedded across product lifecycles, talent development, workflow orchestration and partner ecosystems. Industry alliances, public consultations and advisory forums are playing a critical role in shaping consistent governance standards and ensuring early regulatory alignment.
Building a future where trust scales with innovation
Shadow AI is not a temporary phenomenon. It is a structural challenge that will grow alongside AI adoption. But it is also an opportunity. Enterprises that respond with transparency, visibility and governance embedded by design will move beyond reactive defense and toward proactive trust creation.
The path ahead demands a blend of technology, policy and culture:
– Architectures designed for responsible AI
– Leadership frameworks that encourage governed innovation
– Workforce education that aligns technical practice with business values
As AI becomes embedded across enterprise operations, trust will define which organisations unlock its full value and which struggle to manage its risks.