Skyflow Launches Skyflow for Glean, Bringing Runtime Data Control to Enterprise AI Search

In August 2026, Skyflow recently launched Skyflow for Glean, which is a data security and governance layer designed to control sensitive information as it moves through enterprise AI search and agent workflows.

The offering is aimed at organizations that are increasingly using enterprise search systems to bring together information from sources such as customer relationship management systems, SaaS applications, data lakes and internal wikis. Skyflow also said that its approach is to apply controls not only to stored data, but also when information is retrieved and used as context by AI systems.

The company also stated that the product addresses a challenge that emerges when enterprise AI search expands beyond conventional document retrieval. AI systems can now use underlying information to generate answers, summaries, and reasoning, potentially exposing sensitive fields even when users have permission to access the broader documents.

Skyflow said the controls are intended to allow organizations to make broader use of enterprise information while limiting the exposure of sensitive data in AI-generated answers, summaries and agent interactions. The company also said the platform supports regional data requirements including GDPR, DPDP and HIPAA, with access activity recorded for askyflow-glean-enterprise-ai-searchuditing.

Impact on the AI Market

Skyflow for Glean operates at two stages. During ingestion, the platform detects and tokenizes sensitive information before content is added to the search index and embeddings. During retrieval and agent execution, it applies policies for masking, rehydration and context filtering. The approach is intended to complement Glean’s existing permissions-based access controls by adding controls at the individual data-field level.

Skyflow secures the flow of data across datastores, models, and agents. The platform also helps enterprises protect and govern billions of sensitive customer records while enabling safe use of that data throughout their applications, data platforms, and AI systems. The company is trusted by Fortune 500 enterprises, startups, and leading SaaS companies across financial services, healthcare, retail, travel, and hospitality.

Impact on the Artificial Intelligence (AI) Infrastructure Market

The global artificial intelligence (AI) infrastructure market size accounted for USD 72.02 billion in 2025 and is predicted to increase from USD 91.21 billion in 2026 to approximately USD 518.26 billion by 2035, expanding at a CAGR of 21.82% from 2026 to 2035. 

According to Precedence Research, the market has been expanding steadily due to the rising need for AI-driven solutions in industries including healthcare, banking, retail, manufacturing, and automotive. Because of their capacity for parallel computing, graphics processing units (GPUs) are frequently employed to accelerate artificial intelligence workloads. The increasing ubiquity of cloud computing offers resources that are both affordable and scalable for the implementation of artificial intelligence (AI) infrastructure. By providing AI-specific services and solutions, cloud service providers allow businesses to access strong computing resources without having to make a sizable upfront hardware investment. The artificial intelligence (AI) infrastructure market is growing as a result of the advent of AI companies and a thriving ecosystem of developers, academics, and businesses. These organizations stimulate market expansion by fostering innovation in AI services, software, and hardware.

The demand for real-time AI inference and the growth of IoT devices have also led to an increasing focus on edge computing solutions. By allowing AI inference to be done locally on devices, edge AI solutions improve privacy and security while lowering latency and bandwidth needs. The artificial intelligence (AI) infrastructure market is expanding quickly, but it still faces several obstacles, such as interoperability problems, ethical dilemmas, skill shortages, and privacy difficulties with data. 

Impact on the Artificial Intelligence of Things (AIoT) Market

The global artificial intelligence of things (AIoT) market size accounted for USD 225.89 million in 2025 and is predicted to increase from USD 297.60 million in 2026 to approximately USD 3,248.22 million by 2035, expanding at a CAGR of 30.55% from 2026 to 2035.

According to Precedence Research, the market is rapidly evolving as organizations seek to harness real-time intelligence from connected devices. The Internet of Things and artificial intelligence are combined to create AIoT, whereby AI algorithms evaluate data from networked devices to facilitate quicker, more intelligent, and self-governing decision-making. Businesses can use predictive maintenance techniques to increase asset utilization, lower operating costs, and boost productivity thanks to this integration. The increasing need for automation, real-time analytics, and smart infrastructure is fueling the expansion of AIoT use in the manufacturing, healthcare, transportation, retail, and energy sectors.

Artificial intelligence also plays a crucial role in connected devices like IoT, enabling intelligent automation and real-time decision-making. By integrating AI into IoT systems, businesses can glean useful insights from massive volumes of sensor-generated data. Predictive maintenance is made possible through AI, enabling autonomous control and enhancing the operational efficiency of IoT. Advanced features like pattern recognition, anomaly detection, facial/object recognition, and natural language interaction are made possible by AI, which allows IoT devices to go beyond simple data collection.

Expert Opinion

“We don’t want to be in the data sanitization business,” said Mark Johnson, Partner and Chief Information and Digital Officer at Kearney. “Skyflow gave us per-field encryption, runtime policies, and customer-specific isolation as a platform – not a project.”

Anshu Sharma, co-founder and CEO of Skyflow, said the focus was on controlling data at the point where it is accessed by search and AI systems. “Every search and every agent action touches your most sensitive records,” he said. “Protecting that moment matters as much as protecting the storage behind it.”