21 Oct The Implications of AI for Data Security
As artificial intelligence (AI) continues to transform industries, organizations are increasingly incorporating AI systems to optimize processes, enhance decision-making, and improve customer experiences. However, as with any technology, these systems, while powerful, can become targets for cyberattacks, putting sensitive information at risk. For corporate professionals and government leaders, it’s essential to understand the implications of AI on data security before deciding how to leverage AI effectively.
Why Data Security Is Critical for AI Systems
AI systems rely on large datasets and user inputs to train algorithms and deliver insights. These datasets often contain sensitive or proprietary information—ranging from customer records and financial data to intellectual property. If these AI systems are not properly secured, attackers could manipulate the data, compromise the algorithms, or steal the valuable information processed by the AI.
Even ChatGPT confirmed a data breach in 2023. The breach turned out to be relatively minor—exposing only names, emails, and last four digits only of associated credit card numbers—but that even one of the most prominent AI services could be breached is enough to give AI users pause. In fact, in some cases, data can be rendered insecure even without a breach: if an organization shares proprietary or confidential data with an AI tool or system, that data can potentially become accessible to unauthorized individuals working on the tool or even become part of the tool’s training dataset.
Unlike traditional software systems, AI introduces a layer of complexity that makes securing them more challenging. AI models can be vulnerable to specific threats like adversarial attacks, in which attackers subtly manipulate input data to cause AI systems to make incorrect decisions.
Best Practices for Securing AI Systems
The most important best practice: both those who implement and those who use AI must take security seriously. A survey from the IBM Institute for Business Value (IBV) found that while over 94% of business leaders agree that securing AI is important, but only 24% indicate that their own AI initiatives will incorporate cybersecurity within the next six months. That gap is concerning. “It’s important to make sure that controls are in place so that business and client data don’t get exposed,” says Scott McCarthy, IBM Global Managing Partner for Cybersecurity Services. To secure AI systems effectively, organizations should:
- Implement AI Governance: Establish clear governance protocols for AI usage. This includes regular security audits, risk assessments, and a detailed understanding of how AI is being used across departments.
- Secure the AI Supply Chain: Ensure that AI models or components sourced from third-party vendors are secure and adhere to the same data protection standards that apply to the organization’s internal systems.
- Collaborate with Cybersecurity Experts: Building in-house expertise or collaborating with cybersecurity firms that specialize in AI can help organizations navigate the complexities of securing AI systems. AI-specific attack vectors, such as adversarial machine learning, require specialized knowledge.
Moving Forward: The Stakes of AI and Data Security
The stakes are high when it comes to AI and data security. Organizations stand to benefit significantly from AI’s capabilities, but the risks are equally significant if proper security measures aren’t in place. As AI adoption grows, so will its appeal as a target for cybercriminals. For leaders making decisions about AI, the key takeaway is clear: a strong data security framework must be at the foundation of any AI initiative.
About PSL
PSL is a global outsource provider whose mission is to provide solutions that facilitate the movement of business-critical information between and among government agencies, business enterprises, and their partners. For more information, please visit or email info@penielsolutions.com.
