Partner POV | The Ultimate Guide to Data Security, Privacy, Compliance, and Hygiene for AI

This article was written and contributed by our partner, BigID.

Generative Artificial Intelligence (AI) 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 generative AI, 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 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 vs traditional approaches of prioritizing structured data. Since it's the foundation of AI, understanding and managing unstructured data is now more important than ever - but 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. The only way to manage today and tomorrow's data landscape is with automation and AI.

In order to adapt and innovate at the speed of AI, organizations need to be able to:

• Control what data can be shared, by whom, to which LLMs or AI applications.
• Audit & inspect what data is being shared with LLMs & AI - based on privacy, sensitivity, regulation, and access
• Build out policies of data usage for AI
• Enforce or be alerted when policy is breached.

That's where BigID comes in. Organizations of all sizes leverage BigID 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.

The Critical Role of a Next-Gen Catalog and Inventory Unstructured Data

Generative AI 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 a critical first step to mitigate risk and prepare data for AI. It's the unstructured data - the documents, spreadsheets, text files, emails, messaging content - that's critical here, and is the emerging focus in order 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). Customers can automatically find, label, and tag their data with BigID - streamlining data organization and enhancing the effectiveness of generative AI models.

On top of that, when AI is trained on poor quality data - particularly data that is duplicate, similar, redundant, or obsolete - it can effect the accuracy and security of the results of the AI model itself. BigID's unique capabilities to identify similar and duplicate data make it easy to minimize redundancies in the data, prepare secure data sets for use in AI, and minimize the risk of sensitive data being overexposed.

Sensitive data and dark data requires special handling to comply with privacy regulations, security frameworks, and ethical considerations. To get ahead of the AI era, you need to automatically identify sensitive data of all kinds - whether that's secrets, regulated data, critical data, customer data, intellectual property, etc. - adding the necessary labels, tags, and flags to safeguard your organization and your stakeholders.

BigID automatically identifies sensitive data of all kinds - including secrets and passwords, personal and customer data, financial data, IP, confidential, and more - adding the necessary labels, tags, and flags to safeguard your organization and your stakeholders. With BigID's identity-aware capabilities, organizations can easily contextualize the data across data stores and types, enabling automated discovery, accurate classification and categorization, and automate policies and enforcement across the data - based on risk, location, type, sensitivity, and more.

BigID's stateful inventory not only identifies sensitive data but ensures you have an up to date inventory of the unstructured data alongside structured and semi structured data all in one view - across the cloud and on-premise.

With BigiD, organizations can make sure their data is safe and prepared for AI use - they can control what data can be shared, by whom, to which LLMs or AI applications; they can audit and inspect what data is being shared with LLMs; and they can build out policies for data usage in LLMs and be able to enforce or be alerted when policy is breached.

This ensures that organizations always have a real-time understanding of their data landscape, a critical factor for generative AI success.
 

Risk Identification and Toxic Content Detection

In the era of data breaches and cyber threats, identifying risky data is paramount. BigID's advanced algorithms and machine learning models pinpoint potential risks, including sensitive information exposed to unauthorized access, ensuring that your data is secure for generative AI usage.

In order to curate data for AI, data needs to be clean from personal data, sensitive data and potentially validate it is clean from toxicity and bias.

Toxic combinations - like the presence of a customer ID alongside a credit card number - can have severe consequences if incorporated into generative AI models. BigID actively detects and surfaces toxic combinations, preventing it from contaminating your AI training data.

You can customize what's toxic to your situation: whether that's a combination of name and address; password and credential; ID number and financial statement.  Identifying toxic combinations ahead of AI use is critical to minimizing risk and building security by design within AI adoption.

BigID's approach to risk identification and toxic content detection is grounded in cutting- edge technologies, allowing organizations to proactively manage data risks and maintain the integrity of their generative AI pipelines.

Ensuring AI Ethics and Regulation Compliance

Data privacy regulations, security frameworks, and AI ethics guidelines are constantly evolving, and it can be challenging to keep up.

BigID's compliance capabilities help organizations stay ahead of the curve by automatically applying policies based on data type and regulation, assessing data against the latest regulatory standards and frameworks, detecting compliance violations and recommending corrective actions, and mitigating compliance risks.
 

Once you have these policies in place, it's easier to detect compliance violations while recommending corrective actions, so that you can align your data practices with evolving ethical and regulatory requirements. This ensures that your generative AI initiatives can remain innovative while staying responsible, minimizing risk, and aligning with compliance and the evolving regulatory world.

Prepare Your Data for Generative AI with BigID

In the age of generative AI, data preparation is the cornerstone of success. BigID empowers organizations to catalog and inventory their data, automate data labeling, identify and minimize risks, ensure ethical and regulatory compliance, and streamline data preparation for AI. With BigID, you can confidently embark on your generative AI journey, unlocking the full potential of AI innovation while minimizing risk, meeting ethical and regulatory standards, and driving more value.

Jumpstart your AI journey by elevating data security for AI - with BigID, you can:

• Prepare data as safe for AI and minimize risk of data leaks and breaches
• Automate access governance and control and manage insider risk (even automatically identifying what data different models have access to, not just users),
• Understand what data different models consumed for auditing purposes
• Manage data privacy, compliance, and security for the data that feeds AI
• Enforce controls across the data landscape to maximize the impact of AI while minimizing risk.