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VAST Data has introduced two new services aimed at tightening governance and accelerating optimisation in large-scale artificial intelligence deployments, positioning its software platform as a secure and self-improving operating system for enterprise AI.
The company used its VAST Forward 2026 event to unveil PolicyEngine and TuningEngine, additions to its VAST AI OS that are designed to address mounting concerns around control, explainability and performance in agent-driven systems. The move reflects intensifying competition among infrastructure providers seeking to underpin mission-critical AI workloads across finance, healthcare, research and government.
PolicyEngine is built to govern the behaviour of AI agents operating within an organisation’s data environment. As enterprises adopt generative AI models and autonomous software agents to automate decision-making, questions have emerged about oversight, compliance and accountability. PolicyEngine seeks to enforce guardrails by embedding policy controls directly into the data layer, enabling organisations to define how agents access information, what actions they can take and how outputs are audited.
TuningEngine, meanwhile, focuses on optimisation. It is designed to continuously refine models and data pipelines based on real-world usage, effectively allowing AI systems to adapt over time without compromising governance standards. By combining policy enforcement with performance tuning, VAST Data argues that enterprises can build AI systems that are not only efficient but also trusted and transparent.
The launch comes at a time when regulators across Europe, North America and parts of Asia are sharpening scrutiny of advanced AI systems. The European Union’s AI Act, phased in from 2024 onwards, places specific obligations on providers and deployers of high-risk AI systems, including requirements for transparency, risk management and human oversight. In the United States, federal agencies have issued guidance on safe and secure AI deployment, while industry standards bodies continue to develop frameworks for responsible AI governance.
Against this backdrop, infrastructure vendors are under pressure to move beyond raw compute and storage capabilities. Enterprises increasingly demand integrated solutions that address compliance, explainability and lifecycle management. VAST Data, founded in 2016 and known for its disaggregated shared-everything architecture, has been expanding its focus from high-performance storage towards a broader data platform strategy tailored to AI.
The company’s AI OS is designed to unify structured and unstructured data, enabling real-time access across distributed environments. This architecture has attracted clients in sectors that require both scale and reliability, including financial services firms running fraud detection models and pharmaceutical groups conducting drug discovery simulations.
Industry analysts note that the challenge for organisations scaling AI is no longer limited to training large models. Operationalising those models securely, particularly when they interact autonomously with other systems, is becoming the central hurdle. Agentic AI – systems capable of initiating actions and making decisions without constant human input – raises the stakes. Without embedded governance, such systems could expose sensitive data or trigger unintended outcomes.
By placing PolicyEngine at the data layer, VAST Data is attempting to make governance intrinsic rather than an afterthought. Policies can be defined once and enforced consistently, regardless of which model or application accesses the data. This approach mirrors broader trends in zero-trust security architectures, where access controls are continuously verified rather than assumed.
TuningEngine complements this by addressing model drift and performance degradation. As data distributions shift and usage patterns evolve, models can lose accuracy or efficiency. Continuous tuning aims to maintain alignment with operational goals while respecting the constraints set by PolicyEngine. The company describes the interplay between the two services as creating a feedback loop in which learning occurs within clearly defined boundaries.
Competition in this space is intensifying. Cloud hyperscalers have introduced governance toolkits and managed AI services with built-in compliance features. Chipmakers and systems integrators are also expanding into AI lifecycle management. VAST Data’s strategy appears to centre on differentiating through tight integration between storage, data management and AI control mechanisms, rather than offering governance as a separate overlay.
Executives at the company have framed the announcement as a response to enterprise demand for AI systems that can be trusted in production environments. As generative models are embedded into customer-facing applications and core business processes, reputational and legal risks rise. Data breaches, biased outputs or opaque decision-making can carry financial penalties and erode public confidence.
The emphasis on explainability aligns with broader corporate priorities. Boards and regulators are increasingly asking how AI systems arrive at specific outcomes, particularly in sectors such as banking and healthcare where decisions affect livelihoods and wellbeing. Embedding governance within the data infrastructure could provide clearer audit trails and traceability, helping organisations demonstrate compliance.
