How Dell's AI Security Strategy Uses Layered Cyber Resilience with Druva and Modern Data Protection

As AI systems have evolved, developing a multilayered cyber resilience strategy has become essential for maintaining a strong security posture. Companies that develop a defense-in-depth approach that combines the leading components of AI infrastructure security will have a much greater chance of enduring an increasingly complex threat landscape than those that rely on a single security tool. Here's a look at what implementing AI infrastructure security entails, and how it can facilitate innovation in an AI-powered world.

The Evolution of Defense-in-Depth for Enterprise AI Environments

Cybersecurity needs in enterprise environments that now use AI have shifted. While legacy systems employed fixed perimeters, used limited trust principles, and relied on manual responses to identify and remediate threats, AI environments require automated, zero-trust protection to large-scale datasets, data centers, large language models (LLM), and harnesses, along with every other AI component.

To achieve this comprehensive defense, security teams implement a "defense-in-depth" approach that applies multiple layers of security to create an overlapping AI defense. A few components of a typical defense-in-depth security system include:

Physical controls: Protect data centers, servers, and other physical locations (locks, guards, surveillance cameras).
Network security controls: Let employees enter the network and use a device or application.
Administrative controls: Authorize authenticated employees to access limited applications or parts of the network.
Zero trust security principles: Require continuous verification and authorization, preventing the spread of a threat.
Immutable backups: Prevent corruption in the event of a breach and enable faster disaster recovery.
Antivirus tools: Prevent malicious software from entering the network and spreading.
Behavioral analysis tools: Employ algorithms and ML to detect anomalies in employee, device, and application behavior.

By integrating these controls into a single unified cyber defense, companies better equip themselves to withstand advanced AI-powered threats and can achieve end-to-end AI infrastructure security.

Securing the AI Factory Through Architectural Integrity

A defense-in-depth data protection strategy secures AI factories so companies can turn raw digital inputs into outputs that provide sharper operability. Just as physical factories take raw materials and goods and turn them into a finished material, AI factories take raw materials such as data, electrical power, and hardware accelerators like GPUs, and convert them into usable tokens, AI agents, business intelligence, or newly trained AI models.

Securing such an advanced AI infrastructure requires implementing data governance best practices at the data pipeline, network, and hardware layers:

At the data pipeline layer, implement supply chain tracking to audit pipelines, public training sets, and component hashes to block model poisoning. Use immutable backups and golden copies to ensure proper AI model training and enable faster disaster recovery.
At the network layer, install agentic guardrails that monitor runtime APIs, sanitize inbound/outbound prompts from injection, and audit agent-to-agent communication channels. Apply Zero Trust security principles such as least-privilege access, micro-segmentation, continuous threat assessment and monitoring, and authentication and authorization for every access attempt.
At the hardware layer, create Trusted Execution Environments (TEEs) that employ isolated enclaves to encrypt and protect data and model weights while in use. Build a root of trust by establishing hardware anchors with secure processors and firmware validation to verify boot states.

Organizations must build their data protection strategy into the very architecture on which their AI factories run. That way, security is no longer an add-on, but inherent to AI innovation.

Fortifying AI Data With Druva and Cloud-Native Resilience

AI operations often depend on the kind of large-scale computing only available in the cloud. Cloud-native protection is therefore essential for AI infrastructure security. To bolster cloud-based cyber resilience, Dell collaborates with Druva, an AI-powered, cloud-native SaaS security provider. Through this partnership, Elevate User Community members can avail themselves of their expertise.

As a final line of defense, immutable and air-gapped backups provide protection against remote cyberattacks. Because they are completely detached from the public internet and other unsecured networks, air-gapped systems can only be accessed physically and are closely supervised. Immutable, air-gapped backups often serve as the single source of truth for AI models and enable complete protection of AI training data.

Protecting Data Integrity and Model Provenance in AI Workloads

Even the most advanced AI models can be corrupted if attackers manipulate the data used to train them. Maintaining the highest data integrity is key to ensuring AI models operate as intended. One way to preserve that integrity is to track the location, custody, and history of operations on all data used for training an AI model. Blockchain improves data integrity by enabling better data tracking and provenance, helping security teams understand how certain system hacking incidents have occurred.

Modern data protection also maintains a 'golden copy' of critical AI models, so an uncorrupted version of the original training data is always available, even in the event of a breach. Store them on an air-gapped system to ensure long-term AI viability and maintain cyber resilience against even the most advanced threats.

Practical Considerations for an AI-Ready Security Roadmap

Maturing your security posture requires more than prevention. Reaching true cyber resilience is a journey of continuous monitoring and rapid recovery — and it's a journey you don't want to travel alone.

Dell has recently joined in new partnerships and implemented integrations focused on developing a layered cyber resilience. That includes our work with Druva, an AI-powered, cloud-native SaaS platform that delivers data security, identity resilience, backup protection, and fast ransomware recovery. The result is enhanced AI infrastructure security capabilities and additional security technologies across the AI stack. That means a stronger AI security posture for all members of the Elevate User Community, and a safer AI world for all.