Artificial intelligence (AI) has become an everyday buzzword, and for good reason: it’s significantly changing the way businesses operate and thrive. AI tools are proving to be highly actionable and effective, driving significant improvements in productivity and efficiency. In a recent assessment, Forbes found that 64% of businesses are boosting their productivity with AI while 53% use AI to improve production processes.
However, GenAI introduces new challenges related to data growth and sprawl. IDC’s Global DataSphere predicts that, over the next five years, data will grow at a compound annual growth rate of 21.2%, reaching more than 221,000 exabytes (an exabyte is 1,000 petabytes) by 2026. This data explosion poses a critical challenge even before delving into AI. Addressing data sprawl—its impact on data quality, end-user productivity, and operational costs—is essential to effectively manage expanding data estates and mitigate security risks.
Trusted data throughout the data lifecycle forms the bedrock of successful AI implementation, directly influencing the accuracy, reliability, and integrity of your organization’s AI systems. So, what strategies can companies adopt to effectively harness AI while maintaining data security and ethical practices? Let’s look at some of the best practices.
Establishing data trust in AI
To safeguard the power of AI, organizations need to address the data risks head-on with robust cybersecurity strategies. Only then is it possible to ensure the AI systems are both trustworthy and effective. And establishing trust starts with a comprehensive approach to data and identity management.
Effective AI relies on high-quality, well-managed data. Addressing issues like ROT data—redundant, obsolete, or trivial information—is critical to maintaining data relevance and utility. Privacy concerns are also pivotal as safeguarding the AI training data is fundamental to building trust in an AI systems. By focusing on these elements, organizations can lay a strong foundation of data integrity that supports reliable and ethical AI applications.
Adopting a proven DSPM approach
A proven data security posture management (DSPM) approach is crucial for fostering a secure environment for AI. It’s not just about protecting data but understanding its entire lifecycle, especially as it feeds into AI models. A forward-thinking DSPM strategy involves anticipating and mitigating risks to ensure that AI operates on trustworthy data. This proactive mindset is key to maintaining the credibility of AI-driven insights and sustaining long-term confidence in its outcomes.
Maintaining tight access controls
Managing access to data is a cornerstone for securing data and ensuring AI operates within safe parameters. Utilizing role-based access controls (RBAC) and applying the principle of least privilege are critical steps in creating a controlled and secure environment. By honing 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 am EST. Industry experts will explore these strategies in detail:
Speakers:
- Greg Clark, Director of Product Management, OpenText
- Rob Aragao, Chief Security Strategist, OpenText
Moderator:
- Valerie Mayer, Senior Product Marketing Manager, OpenText
Don’t miss this chance to strengthen trust in your AI initiatives and boost your organization’s data security. Register here!
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