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Generative artificial intelligence (GenAI) has emerged as a game-changer across industries. It enables machines to create content, imitate human intelligence and solve complex problems autonomously.

To fully harness the potential of GenAI, organizations must embark on a journey of data preparation and automation, ensuring that their data is governed, labeled, and compliant with ethical and regulatory standards.

The Shift From Analytics To AI

As the adoption of AI grows, there’s been a shift from analytics to AI, impacting traditional approaches to data cataloging, governance, privacy, security, quality, bias and compliance. This shift from analytics to AI means prioritizing unstructured data versus traditional approaches to prioritizing structured data. Since it’s the foundation of AI, understanding and managing unstructured data is now more important than ever. Still, it can be a challenging hurdle due to the volume, velocity and variety across an organization’s environment.

On top of that, the growing data volume and data change velocity means stewards can’t keep up by using traditional and manual processes. For many, the only way to manage today's and tomorrow’s data landscape is with automation and AI. To adapt and innovate at the speed of AI, many organizations are beginning to:

•Control the data that can be shared, by whom, and to which LLMs or AI applications

•Audit and inspect the data that is being shared with LLMs and AI based on privacy, sensitivity, regulation and access

•Build out policies for data usage for AI

•Enforce or be alerted when policies are breached

Today, it's important for organizations to automatically find, classify, and catalog the data they know about—and the data they don’t—and subsequently minimize risk, prepare data for AI, and automate data management and optimization.

Cataloging And Inventorying Structured And Unstructured Data

GenAI relies on training data, and what’s in that training data can lead to data breaches, leaks, inaccurate decision-making and more. The data AI is trained on needs to be:

•Accurate, up to date and not obsolete or redundant

•Safe for use by purpose, residency and type

•Validated that it doesn’t contain confidential or sensitive information

Data comes in various forms, from structured databases to unstructured content such as files, chats, emails and images. Cataloging and virtually inventorying this diverse data landscape is an important first step to mitigating risk and preparing data for AI. It’s the unstructured data—the documents, spreadsheets, text files, emails and messaging content—that’s critical here. It's also an emerging focus to manage AI responsibly.

Organizations often grapple with the arduous task of categorizing and describing their data (not to mention discovering dark data and shadow data). Sensitive and dark data require special handling to comply with privacy regulations, security frameworks and ethical considerations.

To get ahead in the AI era, it can be effective to automatically identify sensitive data of all kinds, including secrets and passwords, customer data, financial data, IP, confidential and more. This adds to the necessary labels, tags and flags to safeguard organizations and stakeholders.

To get a handle on all of this, organizations need a stateful inventory that not only identifies data but ensures an up-to-date inventory of structured, semi-structured and unstructured data across the cloud and on-premise. This ensures a real-time understanding of the data landscape, a critical factor for GenAI success.

The first step to creating and managing a stateful inventory is to know your data, wherever it lives. Evaluate your ability to scan, tag and classify your data (in place, without moving or copying it) by context and type. This means knowing what type of data it is (is it an invoice? An M&A file?), what’s inside the data (customer information? Secrets and passwords? Credit card data?) and putting policies in place around the data based on that context so that automatic alerts based on the controls around that data are received if it’s moved, deleted or in violation of your business policy.

Risk Identification And Toxic Content Detection

In the era of data breaches and cyber threats, identifying risky data is paramount, especially in unstructured data. Toxic combinations—like the presence of a customer ID alongside a credit card number—can have severe consequences if incorporated into GenAI models.

To reduce risk, organizations need to detect and surface toxic combinations, preventing them from contaminating AI training data. Companies can first evaluate what regulations and security frameworks they can and should adhere to and identify a set of critical data and the subsequent definitions. Regular audits and reports help communicate not just the risk itself but the importance of minimizing that risk while prompting actionable recommendations to reduce that risk.

Ensuring AI Ethics And Regulatory Compliance

Data privacy regulations, security frameworks and AI ethics guidelines are constantly evolving, continuing to be top of mind for security and business leaders. Learn to stay ahead of the curve by automatically applying policies based on data type and regulation, assessing your data against the latest regulatory and ethical standards, and mitigating compliance risks.

Once these policies are in place, it’s easier to detect compliance violations while recommending corrective actions to align data practices with evolving ethical and regulatory requirements. This ensures that GenAI initiatives are both innovative and responsible.

Best Practices For AI Security, Privacy And Compliance

In the age of GenAI, knowing your data is the cornerstone of success. Here are a few final reminders as you begin your GenAI journey:

•Put controls in place based on what the data is and manage your data for AI

•Ensure that your data is prepared to be AI-safe, reducing the risk of leaks and breaches

•Automate access governance and control mechanisms to effectively manage insider risks, going beyond user identification to automatically identify data accessed by different models

•Understand the data consumed by various models for auditing purposes

•Effectively manage data privacy, compliance and security for the data that fuels your AI endeavors

•Implement controls across the entire data landscape

The hopeful result of this journey is to unlock the full potential of AI innovation, minimize risk, meet ethical and regulatory standards, and drive more value. Although the journey isn't an easy one, confidently embarking on it can be done by tailoring this guide to your organization's needs.

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