In the evolving landscape of artificial intelligence (AI) and Machine Learning (ML), data reigns supreme. Organizations are rapidly exploring and leveraging AI to not only streamline their operations and enhance their decision-making, but also to differentiate their products and services in the markets they serve. This digital race encourages speed and agility as companies strive to stay ahead of their competitors, but it can also carry significant risks if the integrity and availability of their data is undermined.
The Value of Data for AI
The value of data as a foundation on which your AI initiatives are created cannot be overstated. It encapsulates insights, patterns, and knowledge extracted from relevant datasets, enabling machines to learn, adapt, and make decisions. Those datasets may also include sensitive input data, intellectual property assets and confidential output reports, that can lead to important insights and enrich the interactions that AI and ML solutions are meant to deliver. This data obviously has significant value that must be rigorously protected with principles, processes, and tools put in place to ensure that it is not improperly accessed or modified.
The Risks Posed by Malicious Actors
Unfortunately, where there is value, there are also those who seek to exploit it for malicious purposes. Cybercriminals are constantly probing for vulnerabilities in AI systems to steal sensitive data, manipulate outcomes, or disrupt business operations. A recent article from NIST highlights and explains these risks in more detail. The consequences of such breaches can be severe, ranging from financial losses and reputational damage to privacy violations, compromised safety, and exposing your organization to legal risks.
Recent studies also underscore the pervasive nature of cyber threats in general, where the data used to advance your AI objectives is just one of many targets. The World Economic Forum's Global Risks Report 2023 identifies cyberattacks as a top global risk in terms of likelihood and impact. In 2023, the FBI received over 880 thousand cyber-crime related complaints with potential losses exceeding USD 12.5 billion. This is a nearly 10% increase in complaints and 22% increase in losses compared to 2022.
The AI Development Lifecycle and Data Protection
Whether you're just starting your AI journey or are already on the right path, it's crucial that you take an intentional approach to protecting and addressing the resiliency of sensitive data at each stage of the AI pipeline as shown in the following image.
Before the development process begins, data is acquired from various sources and collected in a wide variety of forms, such as text files, audio, images, videos, audio, and tabulated data. The acquired data is then cleaned, normalized, and centralized in preparation for use in the modeling, training, and testing stages. During this phase, it is crucial to ensure data integrity and security. Threat scanning, creating immutable copy, access controls, encryption, logical and physical air gap isolation should be leveraged to protect sensitive information in training and test datasets.
Once trained, AI models are evaluated to ensure they meet predefined performance metrics and consistently meet the expected outcomes. After successful evaluation, AI models are deployed into production environments where they interact with real-world data and make predictions or decisions. Due to their high value as intellectual property, it's essential to build multiple layers of threat detection and protection to prevent these models from being deleted, altered, or falling into the hands of malicious actors because of corporate espionage.
The main objective of machine learning is to make consistent and accurate predictions based on data patterns and trends, to respond and solve problems both in business and in the lives of human beings. Whether they are results of medical diagnoses, fraudulent transaction reports, predictive analytics, image recognition, or product recommendations, these results are normally confidential and should be available only to organizations, applications, or people for whom these results have value and to no one else. Threat scanning, immutable copies and data encryption are essential to detect and mitigate security threats and maintain data privacy.
The guidance provided for the AI development lifecycle represent an initial set of suggestions on how you can protect the integrity and availability of your data, models, and AI enabled solutions. It’s not meant to be comprehensive, but it does give you a starting point as you consider how to fulfill that objective. It’s also worth noting that these strategies for data resilience require significant coordination across an array of tools and processes, as there is no one turnkey solution that can address all your potential requirements. As the saying goes, it takes a village.
Protecting Data Across Storage Options and Environments
A comprehensive data protection strategy must address the wide array of storage options and environments in which AI data resides, because it is likely given the volumes of data required that data would need to continuously be moved between higher performance / higher cost storage options and highly scalable / lower cost alternatives. The data protection solutions you use should be able to address the full spectrum of storage required, including primary, secondary, and tertiary storage across on-premises, cloud, edge, and hybrid environments.
The Ethical Imperative
Beyond technical considerations, integrating robust and comprehensive data protection practices into your AI initiatives is an ethical imperative. It requires a commitment to transparency, accountability, and respect for individuals' privacy rights. By embedding data resilience principles and practices into AI systems from the outset, organizations will be in a much better position to foster trust and confidence among stakeholders while mitigating the risks of data breaches or misuse from the broad array of malware that threats that continue to proliferate. Further, it is critical to implement tools and processes that improve your organization’s ability to address current and future regulations targeting data used for AI.
Storage Defender: A solution for end-to-end data resilience
In response to the rapidly expanding threat landscape and the need to protect all your workloads, including AI applications and models, IBM announced IBM Storage Defender one year ago. It provides an end-to-end solution for data resilience that significantly simplifies your management of your data resilience status, shifts threat detection left with AI-powered sensors that can detect data anomalies in virtual machines (VMs), file systems, databases and other applications hosted in Linux VMs. It can provide a unified view of your data protection and cyber resilience status across your hybrid cloud, with support for integration into security dashboards. Storage Defender can then automate and orchestration recovery processes so that you can minimize the disruption caused by a malware attack.
Specifically related to AI workloads, IBM Storage Defender includes OpenShift Fusion backup that supports online backup and restore of watsonx (watsonx.data, watsonx.ai, watsonx.gov, and watsonx Orchestrate). Looking forward, the use RAG (Retrieval-Augmented Generation) and vector databases will certainly be a key consideration
Interested in learning more about IBM Storage Defender? You can explore the full range of capabilities here, where you can sign up for a live demo to see it in action.
Conclusion
In the age of AI, data is both a strategic asset and a potential liability. As organizations increasingly rely on AI to drive innovation and competitive advantage, safeguarding the data used to build, deploy, and manage these solutions at every step and wherever that data is stored becomes paramount. By aligning end-to-end data protection and resilience practices with the major steps of the AI development lifecycle we can mitigate the risks posed by malicious actors and uphold the trust and integrity of AI systems. Only then can you fully realize the potential benefits of your AI initiatives. While building AI-driven solutions can be challenging, you can improve your chances of success by establishing the right foundation for data resilience.
Curious to learn where your data resilience readiness stands? Contact us for a no charge data resilience assessment
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