By Collin McDonald, Director of Solution Architecture
As artificial intelligence becomes an integral part of our digital landscape, IT professionals face a new challenge: securing and managing the vast amounts of data created by AI systems. In this era where AI is no longer a futuristic concept but a daily reality, we’re learning how to effectively safeguard our AI data ecosystems no matter where the data lives.
The Growing AI Data Beast
In our previous exploration of the “Generative AI Minefield,” we dove into the importance of data readiness and security for CTOs. Now, let’s venture deeper into the complex world of AI data, where each path presents new challenges and opportunities.
To set the stage, it’s interesting to note that the scale of AI-generated data is indeed staggering. According to IDC’s Global DataSphere forecast (May 2023), data is expected to more than double in size from 2022 to 2027, growing to 291 zettabytes (ZB) by 2027. That’s a compound annual growth rate of 19.4%. Do you know anything that grows at that rate these days? This exponential increase in data volume presents unique security challenges that organizations must address as they steadily march down the AI adoption path.
Securing the Foundation
As with any sound plan to tame this growing beast before it overtakes us, effectively managing our AI data within our corporate ‘house,’ means we need to first build a solid foundation. The strength of that foundation and the security lies in the preparation and infrastructure. Here are a couple of keys to smart preparation:
- Robust Data Lakes and Supporting Pools: Create comprehensive data lakes and supporting data pools within platforms like Azure or AWS. These serve as the cornerstone for your AI operations. For instance, Azure Data Lake Storage Gen2 offers exabyte-scale storage optimized for big data analytics workloads.
- Scalable Storage Solutions: Implement disaggregated storage to separate compute from capacity. This approach offers flexibility and scalability to meet evolving AI needs. For example, NetApp’s ONTAP AI architecture can scale from terabytes to petabytes without disruption.
AI data needs to be backed by disaggregated storage. It gives you limitless scalability, essential for growing AI operations.
Securing AI-Created Data
According to a Gartner survey cited by FileCloud (2024), 40% of organizations have already experienced AI-related privacy breaches, with one in four being malicious.
Given we’re only in the infancy of this AI-driven data explosion, here are a few more tools to consider deploying sooner vs. later:
- Data Discovery and Classification: Implement tools like Varonis Data Security Platform or Microsoft Purview to automatically discover, classify, and protect sensitive AI-generated data. These solutions can help identify personal information, intellectual property, and other critical data that may be embedded in AI outputs.
- Encryption and Access Control: Use strong encryption for data at rest and in transit. Implement fine-grained access controls using tools like Azure Active Directory or AWS Identity and Access Management (IAM) to ensure only authorized personnel can access sensitive AI data.
- AI-Specific Security Solutions: Consider specialized AI security tools like Lakera Guard or CalypsoAI that can detect and prevent AI-specific threats such as model poisoning or data extraction attacks.
The Power of Hybrid Approaches
While hybrid environments offer scalability and flexibility, they also create new security challenges. The key to leveraging a hybrid approach without losing control of the ‘data monster’ is to determine the best location for various workloads. Once you’ve made these decisions, implement a consistent security policy across all environments using tools like Microsoft Azure Arc. This category of tools provides unified management and security controls for hybrid and multi-cloud deployments, ensuring a cohesive security strategy regardless of where your data resides.
Taking a Measured Approach to Strategic AI Adoption
Before fully embracing AI in your organization, consider these crucial steps:
- Assessment: Evaluate your current server and memory utilization. Use tools like Datadog or New Relic to gain insights into your infrastructure’s performance and capacity.
- In-Depth Analysis: Building on your assessment, examine workload locations and compliance requirements. Consider using a data governance platform like Collibra or Alation to map data flows and identify compliance gaps.
- Cost-Benefit Analysis: With a clear understanding of your current infrastructure and compliance needs, carefully weigh the opportunity costs of different approaches. Use TCO calculators provided by major cloud providers to estimate the long-term costs of various AI infrastructure options.
Best Practices for AI-Generated Data Protection
As you move forward with your AI adoption strategy, implement these best practices to ensure robust protection of your AI-generated data:
- Data Classification: Start by implementing automated data classification using machine learning-based tools. These can help you categorize and tag data based on sensitivity and regulatory requirements.
- Strict Access Control: Once data is classified, use the principle of least privilege (PoLP) and implement just-in-time (JIT) access for sensitive AI data. Tools like CyberArk or BeyondTrust can help manage privileged access to critical AI systems and data.
- Continuous Monitoring: With access controls in place, deploy AI-powered security information and event management (SIEM) solutions like Microsoft Azure Sentinel or Splunk to detect anomalies and potential threats in real-time.
- Regular Security Audits: To complement continuous monitoring, conduct frequent security assessments and penetration testing of your AI systems. Consider using automated tools like Qualys or Rapid7 to scan for vulnerabilities continuously.
- Data Anonymization: Finally, when using sensitive data for AI training, employ advanced anonymization techniques like differential privacy offered with Microsoft Azure.
Where to Begin: Mapping Your AI Data Landscape
The journey to securing your AI data ecosystem starts with a comprehensive assessment of your current environment. This initial step ties together all the previous sections, providing a foundation for implementing the strategies and best practices we’ve discussed.
Begin by conducting a thorough inventory of all your workloads and data classifications across your entire IT landscape. This includes identifying where AI-generated data is created, stored, and processed. Use data discovery tools to scan your networks, databases, and storage systems to locate and classify all data, paying special attention to sensitive information and AI outputs.
This assessment will provide a clear picture of your data ecosystem, helping you prioritize security efforts and allocate resources effectively. It will also inform your decisions about hybrid environments, guide your AI adoption strategy, and shape your implementation of best practices.
Ready to begin securing your AI data ecosystem?
Let’s talk
Contact us today at (800) 544-8877 to learn how we can help you conduct a thorough assessment and develop a customized security strategy for your AI-driven environment.
“As Director of Solution Architecture at MicroAge, Collin McDonald is focused on closing the gap between IT, Sales, and Operations by architecting proven technology solutions designed to solve real-world business problems while boosting sales and increasing productivity.”
Collin McDonaldDirector of Solution Architecture
