In the quiet corridors of corporate IT departments, a new anxiety is beginning to eclipse the fear of ransomware: the prospect of the autonomous AI agent. For the past two years, chief information officers have been relatively comfortable deploying large language models that summarize emails or draft marketing copy—passive tasks with limited blast radiuses. But as the industry pivots toward “agentic AI”—software authorized to execute transactions, modify code, and manipulate databases without human oversight—the risk profile has shifted dramatically. It is within this emerging vacuum of corporate governance that Rubrik, the cloud data security company that went public earlier this year, is positioning its next major play. According to a recent report by The Information, the company is actively testing software designed to act as an “undo button” for artificial intelligence, a fail-safe mechanism allowing enterprises to reverse the actions of malfunctioning or compromised AI agents.

The initiative underscores a critical maturity point for the generative AI sector. While companies like Salesforce and Microsoft race to integrate autonomous agents into the enterprise workflow, the infrastructure required to insure against their mistakes remains virtually nonexistent. If a sales bot hallucinates and offers a 90% discount to the entire customer base, or if a coding assistant inadvertently deletes a production database, the damage is instantaneous. Rubrik’s proposed solution utilizes its existing architecture—immutable data backups—to create a granular recovery system. Rather than rolling back an entire company to yesterday’s backup, which would result in the loss of valid work, the tool aims to surgically reverse only the specific data changes made by the errant AI, leaving human-generated work intact.

As enterprises move from experimental pilots to full-scale deployment of autonomous agents, the demand for cyber-resilience tools that can distinguish between human error and algorithmic hallucination is becoming a boardroom priority.

The technical challenge of “un-doing” AI is far more complex than the “Ctrl-Z” command on a word processor. In a modern enterprise environment, data is interdependent; a change in a customer relationship management (CRM) system triggers automated invoicing, which triggers inventory updates, which triggers logistics orders. As noted in coverage by TechCrunch regarding the broader agentic market, the non-deterministic nature of LLMs means that they do not always act predictably, making the audit trail murky. Rubrik’s approach relies on deep metadata analysis, tracking the specific identity of the “user”—in this case, the machine agent—and isolating the precise timestamps of its interactions. This allows for what industry insiders call “point-in-time recovery” applied specifically to machine identities.

This strategic pivot comes at a crucial time for Rubrik. having debuted on the New York Stock Exchange in April, the company is under pressure to demonstrate that it is more than just a legacy backup provider or a ransomware remediation service. By tethering its future to the adoption of AI, Rubrik is signaling to investors that data security is the foundational layer upon which the AI economy must be built. Analysts at Bloomberg have previously highlighted that while AI infrastructure spending has boomed, spending on AI-specific security and governance tools has lagged ensuring a potential catch-up cycle that Rubrik intends to capture. The ability to market a safety net for AI adoption could be the catalyst that encourages hesitant Fortune 500 companies to fully unleash autonomous agents.

The threat landscape has evolved beyond external bad actors to include internal synthetic entities, forcing security vendors to treat AI agents with the same zero-trust scrutiny applied to unknown network devices.

The necessity for such a tool is amplified by the rise of “prompt injection” attacks, a vector where malicious actors manipulate an AI’s inputs to force it to bypass safety protocols. If a hacker successfully hijacks an internal financial agent, the speed at which funds could be moved or data corrupted outpaces human reaction time. According to security research cited by Wired, traditional firewalls are ineffective against these semantic attacks because the traffic looks legitimate. Rubrik’s thesis is that if you cannot guarantee prevention, you must guarantee resilience. The concept mirrors the philosophy that drove their ransomware success: assume the breach will happen, and focus on how quickly the organization can return to a pristine state.

However, the implementation of this technology faces significant hurdles. To effectively undo an agent’s actions, Rubrik’s software requires deep integration into the applications where these agents live—platforms like Salesforce, ServiceNow, and Microsoft 365. While Rubrik has strong partnerships, particularly with Microsoft, the technical friction of mapping data dependencies across fragmented cloud environments is immense. A report by The Wall Street Journal on enterprise software complexity notes that CIOs are increasingly wary of adding more layers to their tech stack unless the value proposition is immediate and clear. Rubrik must prove that its “undo button” works seamlessly without slowing down the very AI velocity companies are paying for.

Rubrik’s development of AI remediation capabilities places it in direct competition with hyperscalers who are simultaneously building their own governance features, threatening to commoditize the safety layer of the AI stack.

The competitive landscape is rapidly crowding. Hyperscalers like Microsoft and AWS are not sitting idle; they are building governance and lineage tools directly into their AI foundries. For Rubrik to succeed, it must offer a cross-platform solution that creates a unified safety layer effectively functioning as a Switzerland of data recovery. Industry commentary on X (formerly Twitter) from prominent CISOs suggests a preference for third-party security vendors over relying solely on the platform providers to police themselves. This “separation of church and state”—keeping the backup and recovery distinct from the operational platform—remains a core tenet of enterprise risk management that plays to Rubrik’s advantage.

Furthermore, the definition of an “error” in the context of AI is fluid. Unlike ransomware, where encryption is clearly malicious, an AI agent might make a suboptimal decision that is technically valid but strategically disastrous. Rubrik’s testing purportedly involves identifying anomalies in data modification patterns. For instance, if a marketing agent suddenly alters the pricing metadata for 5,000 SKUs in three minutes, the system would flag this as a deviation from the norm. As detailed in The Information’s reporting, the goal is to provide IT administrators with a dashboard where they can visualize the agent’s impact and execute a rollback with a single click, turning hours of forensic data restoration into a minutes-long process.

The financial implications of algorithmic failure are driving a new insurance logic in the enterprise, where the cost of a recovery tool is weighed against the potential liability of an unsupervised agent.

The economic stakes of this technology are massive. As companies reduce human headcount in favor of AI agents for customer support and back-office operations, the “human in the loop” safeguard is being removed. This removal creates a liability vacuum. Legal experts cited in the Financial Times have warned that corporations could face shareholder lawsuits if AI hallucinations lead to material financial losses. In this context, Rubrik is not selling software; it is selling corporate indemnification. The “undo button” becomes a compliance requirement, a necessary box to check to satisfy internal audit committees and external insurers who are increasingly skeptical of AI risk.

Moreover, the data gravity involved in this proposition is substantial. Rubrik sits on exabytes of enterprise data. By analyzing how this data changes over time, they possess a unique vantage point to train their own models to detect what “healthy” data looks like versus data corrupted by a rogue agent. This creates a flywheel effect: the more customers use Rubrik to secure their AI, the better Rubrik’s detection algorithms become at identifying subtle corruptions. This data advantage is difficult for new entrants to replicate, potentially cementing Rubrik’s status as a legacy player in the nascent AI security market.

While the promise of instant remediation is alluring, the ultimate success of Rubrik’s initiative will depend on its ability to handle the complex, cascading data dependencies that define modern cloud architecture.

Critically, the “undo” functionality must handle the ripple effects of data modification. If an AI agent erroneously approves a loan, and that approval triggers a fund transfer and a tax record generation, reversing the approval alone is insufficient. The system must be intelligent enough to trace the lineage of that transaction and unwind every downstream consequence. This level of transactional integrity is historically the domain of database administrators, not backup vendors. Rubrik’s challenge is to elevate backup software from a passive storage medium to an active, intelligent participation in the application logic.

As the industry watches Rubrik’s beta testing, the broader implication is clear: the era of “move fast and break things” is colliding with the reality of enterprise risk. The excitement surrounding agentic AI is palpable, but as Forbes recently noted, the “trust gap” remains the primary barrier to adoption. By building a mechanism to reverse the catastrophic, Rubrik is attempting to bridge that gap. If successful, they won’t just be selling a backup tool; they will be selling the confidence required to hand over the keys of the enterprise to the machine.