Enterprise AI depends on the reliability of its training data. If that data is altered, corrupted, or incomplete, the resulting decisions become unreliable. Keepit believes the solution lies in backup systems that do more than store information—they must also guarantee its integrity.
From backup to a “truth fabric” for AI
Keepit’s new AI Truth Cloud redefines backup as a strategic asset rather than a compliance requirement. The platform offers a complete, immutable, and tamper-proof copy of an organization’s data, stored in a vendor-independent cloud. This independence, the company states, makes it a sovereign source of truth for enterprise AI.
The approach goes beyond storage. AI Truth Cloud verifies data authenticity, provenance, and integrity before it enters any AI system. The aim is to ensure models operate on trustworthy data, allowing organizations to revert to a known-good state if issues arise. Businesses increasingly rely on AI for critical decisions, but those outputs are only as dependable as the data behind them.
“Organizations are making high-stakes decisions based on what their AI tells them,” said Frederik Schouboe, Keepit’s co-founder and chief visionary officer. “But unverified data shouldn’t guide actions. AI Truth Cloud provides an independent, tamper-proof foundation—one that AI can safely use and organizations can prove is intact. When a decision fails, they can roll back to a verified state. Owning the truth means verifying it before acting and recovering it when needed.”
Backup systems have evolved before. The industry moved from tape-based archives to disaster recovery and cyber resilience. Now, Keepit positions AI Truth Cloud as the next step—a proactive trust system rather than a passive safety measure.
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Most enterprise data protection still treats backup as a last resort. Yet if AI systems depend on training data, the ability to verify and recover that data becomes essential.
Five pillars of data trust
The AI Truth Cloud platform rests on five core functions: Protect, Observe, Recover, Prove, and Integrate. These replace traditional backup with a framework designed to ensure trust at every stage.
Three capabilities form the initial roadmap. The first, AI Connector Backup, extends protection to AI tools, covering agent configurations, skills, projects, and models. It includes point-in-time restore for any AI asset. If an agent behaves unexpectedly or is compromised, the immutable backup serves as the verified recovery point.
The second, Keepit MCP (Model Context Protocol), functions as a headless API layer connecting Keepit to AI tools or workflows. It enables AI systems to query, audit, and interact with managed data programmatically. This positions the platform as an active participant in enterprise trust rather than a passive repository.
The third, AI Safe Room, addresses a key risk in enterprise AI: running models on live production data. Instead, it provides an immutable copy of data as an isolated environment for training, inference, and testing. If a model produces unintended results, production data remains untouched, and recovery is immediate.
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Future updates will include behavioral monitoring for AI agents, automated compliance evidence generation, cryptographic data provenance, and AI-powered threat rollback. Each update strengthens the platform’s role as the trusted data layer for enterprise AI.
Expanding the foundation
AI Truth Cloud builds on Keepit’s existing strategy. The company plans to broaden its connector coverage as organizations adopt AI-driven workflows, focusing on protecting the data AI agents create and use.
It is also launching an ISV partnership program. This allows software vendors to embed sovereignty, immutability, governance, and trusted AI capabilities into their products through deep integrations with the Keepit platform. The goal is to establish the system as the underlying trust layer across enterprise software.
Adoption at scale remains uncertain. But as AI’s role in decision-making grows, the ability to verify and recover data may shift from optional to essential.
