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With the adoption of artificial intelligence (AI), especially generative AI in the form of large language models (LLMs), across business sectors, the job of data protection has also evolved. The datasets behind genAI workloads are significantly larger, more scattered, and more sensitive than what traditional backup and recovery tools were designed to handle.
When you include training data, prompts, model output, and the systems required to move all of this data back and forth, you can easily see how an adaptable AI security platform is crucial to your data protection strategy. The complexity inherent in this landscape is why more and more teams are looking for a platform capable of protecting their AI infrastructure, data classification schemes, and governance. And being able to recover fast after an outage or breach is a bonus you shouldn’t overlook.
AI protection is a stack of capabilities that need to work together across backup, classification, access control, and recovery workflows. The best AI security platforms bring these functions together in one place so you can protect the systems feeding your AI without slowing down the work dependent on them. The following sections are intended as brief overviews of some of the main players in this space to help get your search underway.
Best for: Enterprises needing unified protection, classification, and recovery across hybrid environments feeding GenAI workloads.
Cohesity’s Data Cloud is built to protect and secure enterprise data across cloud, on-premises, and SaaS environments. For AI use cases, it helps organizations protect the data foundation underneath the training, retrieval, and GenAI workflows while supporting recovery and AI-ready data uses. The platform supports the broader topics of cyber resilience, data security, and AI-ready data, making it relevant for teams looking for a single platform for protection and recovery across mixed environments.
Key Features:
Best for: Organizations prioritizing recovery speed and data classification for AI-related datasets across hybrid infrastructure.
Rubrik Security Cloud is an AI security platform built around the fundamentals of resilience, recovery, and control spanning data, identity, and AI. For AI workloads, the platform’s value is in helping teams protect the data feeding your models and recover it quickly should an incident interrupt access or data integrity. Rubrik describes the Security Cloud platform as a single system unifying data, identity, and AI, with fast recovery across hybrid infrastructures and environments. This combination makes it a good fit for organizations needing a strong recovery posture for their AI-related datasets.
Best for: Large enterprises with strict governance requirements needing discovery, classification, and recovery across dispersed AI data sources.
Commvault Cloud is positioned as a cloud-native platform for data security, identity resilience, and cyber incident recovery. For AI workflows, it is relevant because it connects discovery, classification, governance, and recovery in a unified environment. Commvault says the Cloud platform can find and classify sensitive data across data lakes and application repositories while helping to maintain clean training data that is fully governed and ready for responsible use in your AI workflows.
Best for: Cloud-first and SaaS-heavy organizations needing straightforward backup and recovery for distributed AI data sources.
Druva Data Security Cloud is another cloud-native option focused on data protection, ransomware recovery, and data governance. For AI workloads, it is most relevant when teams need backup and recovery across distributed cloud and SaaS data. Druva’s value in an AI context is tied to protecting the data sitting in SaaS apps, cloud workloads, and endpoints. This makes recovery straightforward no matter the degree of disruption or location of impacted systems.
Best for: Organizations already running Veeam that want to extend existing backup coverage to AI-supporting infrastructure.
Veeam Data Platform is widely used for backup and recovery across virtual, physical, and cloud environments. For AI-specific workloads, it is often considered when teams need dependable recovery for data storage infrastructure supporting model operations. Veeam is strongest where organizations already use it for backups and want to extend coverage to their AI systems. The primary question is how well it fits your mix of data sources, recovery targets, and governance needs.
Best for: Security teams focused on understanding data exposure and classification risk across AI pipelines before selecting a backup layer.
Cyera Data Security Platform focuses on data discovery, classification, and security posture management. For AI programs, this matters because teams need to know what data they’re protecting, where it lives, and which AI systems can access it. Cyera is often included in evaluations of AI data security platforms because it helps teams map data exposure across hybrid environments so they can better understand the risks tied to their specific AI use cases.
Best for: Organizations where AI governance and training data compliance are the primary concern ahead of backup and recovery.
BigID Data Intelligence Platform is built around discovery, classification, privacy, and security. For AI workloads, it is a contender because model quality and compliance depend on knowing what data is in play, and how it’s being used. BigID is often used to map sensitive data, identify risk, and support governance programs spanning structured and unstructured data. In AI projects, this helps teams control what data goes into training sets and what data is left to other uses.
Best for: Teams focused on access control and insider threat visibility for the data sources behind their AI programs.
Varonis Data Security Platform is primarily known for data governance, access control, and threat detection. Access to data sources often makes the difference between data remaining safe and it being accidentally or intentionally exposed. Varonis is good for teams trying to control who can reach sensitive files, where permissions have drifted, and how data moves between collaboration and storage locations.
AI data protection only works when the platform covers the full path from source data through to recovery. The right AI security platform should protect infrastructure, locate and identify sensitive data stores, govern access, and recover AI systems fast enough to maintain real-world business operations.
AI programs depend on more than the model itself. Training datasets, feature stores, prompt logs, checkpoints, and supporting services all need protection since losing any one of them can interrupt or derail development or lead to corrupt results.
A strong AI data protection platform should back up all of these assets in a way that fits the speed and scale of your AI workflows. This includes support for high change rates, large datasets, and mixed environments spanning on-premises and cloud installations.
At the risk of repeating ourselves, you can’t protect what you can’t see. Classification helps teams identify sensitive data across AI pipelines before it’s used in model training, retrieval-augmented generation, or analytics workflows.
This is where data security posture management (DSPM) helps organizations discover and classify sensitive data across environments, a key step in reducing exposure before data enters AI systems. For AI teams, classification also supports policy decisions around what can be used, where it can be used, and who can use it.
AI systems often connect to many data sources at once, which can widen exposure if access controls are loose or missing. Governance should cover service accounts, users, third-party tools, and the permissions that control the source systems feeding into your model.
A useful platform should show who can reach the data, how those permissions may have changed over time, and whether the access patterns fit policy. This goes doubly for GenAI environments, where a single broad connector can expose more content than intended quite easily.
Recovery is different for AI systems than for ordinary file restoration. Teams need to think about model artifacts, vector databases, embeddings, and the underlying training datasets that may need to come back together and in the right order to be functional after recovery.
A platform should support clean recovery of all components, not just storage volumes. If a vector database or model dependency is corrupted, you need a clear path to restore the full environment without necessitating weeks of rebuilding.
The evaluation process for the right AI data protection platform should start with identifying how broad the platform’s coverage truly is. It should handle both structured and unstructured data, integrate with the systems you already have in your stack, and support the right mix of hybrid and multicloud AI environments without creating any unnecessary cleanup work.
Next, it’s important to benchmark recovery with real-world AI scenarios rather than generic backup tests. Use training datasets, vector databases, and model-supporting services in your test plan to check whether the recovery workflow can restore everything with full integrity. For teams building search or conversational AI experiences, AI-ready data is a useful lens through which to decide whether a platform supports your needs.
Evaluation quick tips:
Cohesity addresses AI data protection across the full lifecycle, from discovery and classification through to backup and recovery. Data Cloud is built to protect hybrid and SaaS workloads, improve recovery speed and accuracy, and to support secure enterprise data ready for all of your GenAI projects.
AI programs need more than storage protection to remain viable. They need a platform that can help reduce exposure risk, keep data AI-ready, and restore fast when something goes sideways. Explore Cohesity’s AI-powered data security to see how our platform can support your efforts to secure your AI-ready data at scale.
