Artificial intelligence (AI) has become a buzzword, and for good reason: it is dramatically changing the way businesses operate and thrive. AI tools are proving to be highly practical and effective, leading to significant improvements in productivity and efficiency. In a recent assessment, Forbes found that 64% of companies are increasing productivity with AI, while 53% are using AI to improve production processes.
However, GenAI introduces new challenges related to data growth and sprawl. IDC’s Global Datasphere The institute predicts that over the next five years, data will grow at a compound annual rate of 21.2%, reaching more than 221,000 exabytes (one exabyte equals 1,000 petabytes) by 2026. This explosion of data poses a significant challenge, even before we consider AI. Addressing data proliferation—its impact on data quality, end-user productivity, and operational costs—is critical to effectively managing expanding data estates and mitigating security risks.
Trusted data throughout the data lifecycle is the foundation of successful AI implementation, directly impacting the accuracy, reliability, and integrity of your organization’s AI systems. So what strategies can businesses adopt to effectively leverage AI while maintaining data security and ethical practices? Let’s look at some best practices.
Building Trust in AI Data
To preserve the power of AI, businesses must address data risks with robust cybersecurity strategies. Only then can they ensure that AI systems are both reliable and effective. And building trust starts with a holistic approach to data and identity management.
Effective AI relies on high-quality, well-managed data. Addressing issues like ROT (redundant, stale, or trivial information) data is critical to maintaining the relevance and usefulness of the data. Privacy concerns are also critical, as protecting AI training data is fundamental to building trust in AI systems. By focusing on these elements, organizations can establish a strong foundation of data integrity that supports trustworthy and ethical AI applications.
Adopting a proven DSPM approach
A proven data security posture management (DSPM) approach is essential to fostering a secure environment for AI. It’s not just about protecting data, but understanding its full lifecycle, especially as it feeds AI models. A forward-thinking DSPM strategy involves anticipating and mitigating risks to ensure AI is operating on trusted data. This proactive mindset is essential to maintaining the credibility of AI-generated insights and maintaining long-term trust in its outputs.
Maintain strict access controls
Managing data access is a cornerstone for securing data and ensuring AI operates within safe parameters. Using role-based access controls (RBAC) and enforcing the principle of least privilege are essential steps to creating a controlled and secure environment. By perfecting these aspects of identity and access management (IAM), organizations can foster a controlled environment that ensures the secure and ethical use of AI technologies.
To dive deeper into these best practices, join our upcoming webinar on September 17 at 11:00 a.m. EST. Industry experts will explore these strategies in detail:
Speakers:
- Greg Clark, Director of Product Management, OpenText
- Rob Aragao, Chief Security Officer at OpenText
Moderator:
- Valerie Mayer, Senior Product Marketing Manager, OpenText
Don’t miss this opportunity to build trust in your AI initiatives and improve your organization’s data security. Register here!
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