AI: The emerging threat surface

Artificial Intelligence (AI) has revolutionized the way that organizations operate, enabling them to automate processes, gain valuable insights, and make data-driven decisions. However, as AI is increasingly integrated into business systems and applications, it also introduces a new and expanding threat surface that cybercriminals are eager to exploit. In this rapidly evolving landscape, ensuring robust security at the storage layer is more critical than ever to safeguard sensitive data and protect organizations from devastating cyberattacks. 

AI systems rely heavily on vast amounts of data to train models, make predictions, and support decision-making processes. This data often includes sensitive information, such as personally identifiable information (PII), financial records, and proprietary business insights. As AI becomes more deeply embedded in an organization's infrastructure, the volume and sensitivity of the data it processes and stores continue to grow, making it an attractive target for cybercriminals. 

Attackers can exploit vulnerabilities in AI systems to gain unauthorized access to this valuable data, potentially leading to data breaches, intellectual property theft, and reputational damage. Additionally, by manipulating the data used to train AI models, attackers can compromise the integrity of the AI system itself, causing it to make incorrect decisions or take malicious actions. 

The complex nature of AI systems, which often involve multiple components, libraries, and APIs, creates a larger attack surface compared to traditional software applications. Each component introduces potential vulnerabilities that attackers can exploit to gain a foothold in the system and move laterally to access sensitive data. 

As AI systems handle ever-increasing volumes of sensitive data, securing this data at the storage layer becomes of paramount importance. The storage layer represents the final line of defense against cyberthreats, because it is where data resides and is accessed by AI systems. By enhancing the overall security posture with robust security measures at the storage layer, organizations can ensure that their data remains protected even if other layers of defense are breached. The NetApp^® built-in capabilities for securing data across the hybrid environment help to protect, detect, and recover data quickly. 

Although securing data at the storage layer is crucial, it’s also essential to recognize that storage layer security is just one component of a comprehensive AI security strategy. Organizations must adopt an integrated approach that addresses security across the entire AI lifecycle, from data acquisition and model training to deployment and ongoing monitoring. 

• Secure data pipelines. Ensuring the security and integrity of data as it moves from its source to the AI system, protecting against data tampering and unauthorized access. 

• Secure model training. Implementing measures to prevent attackers from manipulating training data or compromising the model training process. 

• Secure AI deployment. Protecting AI models and their associated infrastructure from unauthorized access, tampering, and exploitation. 

• Continuous monitoring. Monitoring AI systems in real time to detect anomalous behavior, performance degradation, or potential security breaches. 

• Incident response and recovery. Developing and regularly testing incident response plans to quickly detect, contain, and recover from AI-related security incidents. 

Protecting the AI runtime from manipulation 

Built-in cyber resilience can help protect your data from threats you don’t even know exist. By adding multiple layers of protection, NetApp’s cyber-resilience features help maintain the integrity, availability, and security of AI runtime. 

Mitigating the risk of training data poisoning 

Poisoning of training data can be particularly insidious because it can be difficult to detect, especially if the changes to the data are subtle and well-crafted to blend in with legitimate training examples. NetApp’s comprehensive data protection mechanisms, including backup, recovery, and encryption, maintain the integrity of training data and prevent unauthorized alterations that could lead to data poisoning. Our AI and machine learning driven health monitoring also detects unusual patterns or anomalies in your data storage environment, so you can identify attempts at data poisoning and intervene before the compromised data is used for training. 

Preventing model theft 

NetApp’s cyber-resilience solutions enable administrators to define policies that specify which file operations are allowed or denied, and under what conditions. You can track file access and changes, creating an audit trail that can be analyzed for signs of model theft or data poisoning, while also supporting compliance efforts and aiding future forensic analysis. 

By adopting an integrated approach to AI security, organizations can effectively manage the risks associated with AI and protect their most valuable data assets.  

Explore more in the white paper Introduction to the NetApp Security Framework by NetApp security expert Justin Spears.