What actually happens when AI mishandles your data? In 2023, a glitch in ChatGPT accidentally exposed payment details and chat histories of users to complete strangers. That one bug sparked a massive debate around data security in AI—and rightly so. According to IBM’s 2024 report , the average cost of a data breach touched $5.17 million globally. And when AI is involved, things get even trickier.
This blog highlights key insights from a recent webinar hosted by Kanerika and Concentric AI. Data security and governance experts Naren Babu (Kanerika) and Pedro Ferreira (Concentric AI) broke down how Microsoft Purview helps tackle these modern risks, from shadow AI to compliance blind spots.
If your business is using AI—or thinking about it—this information can save you from costly security breaches or compliance risks.
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The Cost of Ineffective Data Governance in AI: OpenAI’s ChatGPT Incident What Happened? On March 20, 2023, OpenAI’s ChatGPT faced a major glitch that exposed user data, including sensitive personal and payment information. Due to a bug in the system, some users received emails meant for others—containing partial credit card details, names, email addresses, and payment info.
The incident happened between 1 AM and 10 AM Pacific Time. Users reported seeing other people’s chat histories, and some got confirmation emails with someone else’s subscription details.
What Went Wrong? The root of the issue was a bug in the Redis client library (redis-py). Redis is a tool used to manage data requests between the app and the server. The way it handled incoming and outgoing requests caused problems when some were canceled mid-process.
Here’s how it played out:
ChatGPT uses two queues—one for requests, one for responses. If a request got canceled too soon, before its response was handled, the connection got messed up. This mix-up caused data from one user to be sent to another, exposing personal info. A new update pushed at 1 AM PT triggered a spike in canceled Redis requests, increasing the chances of this bug affecting users.
What Kind of Data Was Exposed? The data leak included:
Credit card type and last four digits It was a sharp reminder of how AI systems—while advanced—can still be vulnerable without strong data handling rules in place.
How Was It Fixed? OpenAI took the following steps:
Fixed the bug in the redis-py library. Hardened the Redis cluster to better handle heavy traffic. Enhanced logging to monitor any further issues. Ran extensive tests to make sure the fix held up under pressure. Added extra checks to prevent similar leaks in the future. Why This Matters for AI Data Governance This incident wasn’t just a one-off bug. It highlighted a deeper problem—the lack of clear data governance and privacy safeguards in AI tools. Sensitive data was left exposed because there weren’t enough controls around how it was handled, stored, and accessed.
Without proper systems like Microsoft Purview in place—offering tools like data classification, DLP, and audit logging—these kinds of incidents are bound to repeat, especially as more businesses start using AI in everyday operations.
Data Governance Risks These risks stem from the lack of rules, oversight, and clarity around how data is managed in AI systems.
1.Regulatory Non-Compliance
AI systems often process large volumes of personal or sensitive data , which brings them under the radar of laws like GDPR, HIPAA, or PCI-DSS. Without proper documentation, consent tracking, or data handling rules, it’s easy to end up non-compliant—risking fines, lawsuits, or shutdowns.
2. Data Quality Issues
AI relies on large volumes of data. If that data is incomplete, outdated, duplicated, or incorrect, it can lead to flawed AI decisions and outcomes. Worse, poor quality data can hide risks or create blind spots for compliance teams.
3. Lack of Oversight
Without regular monitoring or accountability, AI systems can end up accessing or processing data in ways no one planned for. Shadow use of AI (tools adopted by teams without IT’s knowledge) adds to the problem, leaving critical gaps.
Security Risks Security risks are mostly about how data can be accessed or exposed—intentionally or by accident.
1. Unauthorized Access
This happens when people gain access to AI tools or the data they use without permission. It could be insiders snooping or third parties getting through weak defenses. AI often has broader access to sensitive data than necessary, making this a big risk.
2. Model Vulnerabilities
AI models can be tricked. For example, attackers can “poison” training data or reverse-engineer inputs and outputs to steal data. If not carefully tested and secured, AI models themselves become weak spots.
3. Shadow AI
Teams sometimes use external AI tools without telling the IT or security team. This “shadow AI” means company data could be processed by unknown, unsecured tools, making tracking and control nearly impossible.
Privacy Risks These are about personal and sensitive data—how it’s handled, and whether the right permissions are in place.
1. Data Leakage
AI models can accidentally “remember” and expose parts of the data they were trained on. If that data includes personal info or confidential records, it can show up in future queries or outputs.
2. Inference Attacks
Even if raw data isn’t exposed, attackers can use smart prompts or analysis to infer private details from an AI model’s responses. For example, guessing user identities or sensitive business logic from patterns in answers.
3. Lack of Consent Management
Many AI systems process user data without checking whether they have the right to. There’s often no proper way to collect, record, or manage consent—especially in fast-paced AI tools—which can quickly lead to compliance violations.
Microsoft Purview Information Protection: What You Need to Know Explore how Microsoft Purview Information Protection safeguards your data with advanced classification, labeling, and compliance tools, ensuring secure and seamless data management .
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What “Data security” Means in the AI Context? Data security in the AI context refers to protecting sensitive information used to train AI systems and the data these systems interact with. It involves safeguarding against unauthorized access, data breaches , and misuse of information. This includes encrypting data , controlling access to datasets, implementing proper authentication systems, and ensuring compliance with privacy regulations.
For AI specifically, this means protecting both the training data that shapes an AI’s capabilities and any personal or sensitive information the AI processes during operation. Strong data security helps prevent model poisoning, data leakage, and maintains user trust in AI systems.
1. Designed for Static Systems, Not Dynamic AI Traditional data security tools were built for structured systems—databases, file servers, and known user workflows. AI, on the other hand, deals with unstructured data , unpredictable patterns, and constantly changing models. This mismatch leaves huge gaps.
2. Limited Visibility into AI Activity Conventional tools can’t monitor how data flows in and out of AI systems. They miss context—like whether an AI tool is storing sensitive information, or if users are pasting company data into unapproved apps.
3. No Control Over Model Behavior Unlike rule-based systems, AI can generate new content based on old inputs. If that input contains sensitive data, traditional DLP tools may not catch it before it’s exposed again.
Microsoft Purview: Mitigating Data Security, Governance, and Compliance Risks in AI Microsoft Purview helps organizations stay in control of their data across AI tools by combining strong data governance , security, and compliance features. It enables sensitive data discovery, automated classification, real-time risk detection, and policy enforcement across cloud and on-prem environments. With built-in AI-focused capabilities like DSPM for AI, Purview ensures your data stays protected, compliant, and well-managed—even in fast-moving, AI-driven systems.
Data Security Capabilities of Microsoft Purview 1. Data Loss Prevention (DLP)
Microsoft Purview’s DLP helps prevent sensitive data from being exposed—whether it’s shared through emails, stored in the cloud, or accessed through devices. It watches how content is used and stops leaks before they happen.
Monitors and blocks sharing of sensitive content across platforms. Works across cloud, on-premises, and endpoint devices. Enforces policies in real-time to stop risky behavior. 2. Information Protection
This capability focuses on labeling and safeguarding important data throughout its lifecycle. It helps classify, encrypt, and apply access rules to sensitive content, even as it moves across users or systems.
Automatically discovers and tags sensitive data (PII, financials, etc.). Applies encryption and access controls based on sensitivity. Protects content from creation to deletion. 3. Insider Risk Management
Purview helps spot and act on insider threats—like employees accessing data they shouldn’t or moving it outside the company. It uses behavioral signals to detect patterns before things go wrong.
Detects risky or unusual user behavior. Flags potential data theft or policy violations. Helps security teams act quickly with detailed alerts. 4. Data Security Posture Management for AI (DSPM for AI)
With the rise of AI tools like ChatGPT and Copilot, Purview’s DSPM for AI feature provides much-needed visibility and control over how sensitive data interacts with generative AI systems.
Tracks AI-related data usage across 357+ AI sites. Blocks or flags sensitive content flowing into AI tools. Microsoft Purview: Risk and Compliance Capabilities 1. Communication Compliance
This feature helps organizations detect and respond to inappropriate or risky messages across communication platforms. It enables proactive management of communication-related risks in line with internal policies.
Monitors employee communications for policy violations. Flags content related to harassment, threats, or sensitive data sharing. Supports platforms like Microsoft Teams, Exchange, and Yammer. 2. Information Barriers
Information Barriers allow organizations to limit communication between selected groups to prevent data leakage or conflicts of interest. It’s especially useful in regulated industries like finance and law.
Restricts chats, calls, and file sharing between chosen departments. Ensures compliance with regulations such as FINRA and GDPR. Helps avoid insider trading risks in M&A or investment roles. 3. Compliance Manager
Compliance Manager offers a centralized view of your compliance posture with real-time scoring and recommendations. It simplifies risk assessments and supports documentation for audits.
Includes pre-built templates for industry regulations. Offers improvement actions to close compliance gaps. 4. eDiscovery
eDiscovery helps legal and compliance teams find, preserve, and export data relevant to investigations or legal cases. It streamlines response efforts while reducing manual workload.
Reduces time and cost associated with data review. 5. Records Management
This feature allows you to automate how long important records are kept and when they’re deleted. It ensures compliance with legal, business, or regulatory requirements.
Applies retention labels to content across services. Supports event-based retention and defensible deletion. Helps avoid accidental loss or data hoarding. Microsoft Purview: Data Governance Capabilities 1. Data Catalog
Data Catalog builds a structured inventory of data assets with rich metadata. It helps users easily discover, understand, and make decisions based on trustworthy data.
Centralizes metadata across various data sources. Supports data search, discovery, and lineage tracking. Enables tagging and annotation for better asset context. 2. Data Lifecycle Management
This capability automates how data is retained and removed based on policies. It ensures that only necessary data is kept, while outdated or redundant content is safely deleted.
Reduces data clutter and storage costs. Helps enforce retention schedules for legal and compliance needs. Applies rules automatically across environments. 3. Data Policy Management
Data Policy Management offers a central place to define and enforce rules for data access and usage. It ensures consistent controls across different teams and systems.
Controls SQL access and DevOps permissions centrally. Ensures sensitive data is only accessible to the right people. Helps enforce policy-based governance at scale. 4. Data Map
Data Map gives a clear visual of how data moves and connects across your systems. It brings structure to your metadata and helps you track data lineage .
Shows relationships between data sources and assets. 5. Audit and Workflows
Audit tools help track user and admin activity, while workflows automate repetitive governance tasks. Together, they support compliance and operational efficiency.
Provides logs for auditing and forensic investigations. Tracks activity across Microsoft 365 services. Data Governance Pillars: Building a Strong Foundation for Data-Driven Success Discover the key pillars of data governance that enable organizations to achieve accuracy, compliance, and success in a data-driven world.
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Microsoft Purview DSPM for AI DSPM for AI (Data Security Posture Management for AI) is a specialized feature within Microsoft Purview designed to enhance the security and compliance of AI systems. This capability enables organizations to track, manage, and secure sensitive data used in AI tools and applications.
As AI tools like Microsoft Copilot and ChatGPT become more integrated into business workflows, DSPM for AI provides visibility into data usage and behavior, ensuring that sensitive information is not exposed or mishandled.
By leveraging DSPM for AI, businesses can:
Enforce data protection policies , including data loss prevention (DLP) and access controls. Microsoft Purview currently supports DSPM for AI across 357 AI platforms globally. It applies DLP policies, monitors unethical behavior, and creates sensitivity labels for AI-generated content—giving organizations better control and confidence as they expand AI usage across their teams.
Framework for AI Governance: Key Phases Involved 1. Discover & Classify The first step is knowing what data you have and where it lives. AI tools can pull from many sources, so it’s important to identify and label sensitive or regulated data early. This forms the foundation for all other controls.
Automatically scan for sensitive info like PII, PCI, PHI, and IP. Ensure coverage across cloud, on-prem, and hybrid systems. 2. Enforce Data Governance Policies Once data is classified, you need clear rules for how it can be used. Governance policies help control access, prevent misuse, and guide responsible data handling. These policies should be automated and regularly updated.
Set rules for who can access and use specific data types. Align policies with business goals and regulatory standards. Automate enforcement across tools and teams. 3. Monitor & Audit Data Usage Keeping track of how AI interacts with data is critical. This phase involves monitoring user behavior and data movement, then logging it for audits. It helps catch issues before they become breaches.
Track access, sharing, and AI model interactions with data. Maintain detailed logs for internal review or external audits. Spot unusual or unauthorized activity in real-time. 4. Establish Accountability and Roles AI governance needs people in charge. This phase focuses on assigning ownership of policies, systems, and incident response. Everyone—from IT to legal—should know their responsibilities.
Define clear roles for data stewards, security, and compliance teams. Create accountability for AI tool usage and data flow. Align roles with escalation paths in case of issues. 5. Implement Data Loss Prevention (DLP) DLP is your safety net—it stops sensitive data from leaving the organization in the wrong way. This includes blocking uploads to AI tools, unauthorized sharing, and risky behavior.
Use policies to prevent exposure of classified data. Block risky transfers across AI tools or external apps. Alert admins to policy violations in real-time. 6. Ensure Regulatory Compliance Different industries face different regulations, and AI can easily cross boundaries. This phase focuses on aligning AI use with privacy laws, industry standards, and internal rules.
Generate reports to prove compliance posture. Respond quickly to audits or legal requests. AI tools aren’t isolated—they need to work within your larger security ecosystem. Integration ensures your governance strategy scales across technologies and environments.
Enable seamless policy enforcement across platforms. Support multi-cloud and hybrid infrastructure. 8. Train and Educate Teams People play a big role in governance. This step is about making sure teams understand the rules, tools, and risks involved when working with AI.
Run regular training for employees on AI data risks. Promote awareness of policies and acceptable AI use. Encourage reporting of suspicious activity or data misuse. 9. Continuously Improve Governance isn’t one-and-done. This phase ensures that policies, tools, and controls are reviewed often to keep up with new risks and changes in AI.
Regularly reassess policies and data risk levels. Learn from incidents to strengthen the framework. Keep up with changes in tech and regulations. Top 10 Data Governance Tools for Elevating Compliance and Security Explore the top 10 data governance tools that enhance compliance, ensure data security, and streamline management for businesses of all sizes.
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Why Data Discovery is Critical for Data Security in AI? Data discovery is the first and most critical step in securing AI systems because you can’t protect what you don’t know exists. AI tools pull data from various sources—structured, unstructured, cloud, and on-prem—which often includes sensitive or regulated information. Without discovery, organizations risk feeding personal, financial, or business-critical data into AI models without oversight.
Discovery supports proper classification, labeling, and policy enforcement. It also helps identify stale or duplicate data, enabling cleanup and reducing exposure. In short, effective AI data governance starts with clear visibility into what data you have and where it’s stored.
Prevent Data Security Risks in Your AI with Kanerika’s Robust Governance Solution As AI adoption accelerates, so do the risks tied to data exposure, poor governance, and regulatory gaps. For enterprises, effective data governance is no longer optional—it’s essential for survival. Kanerika, a leading data and AI solutions company, helps organizations secure their data ecosystems with enterprise-grade tools that go beyond surface-level fixes.
At the heart of Kanerika’s offering is a powerful trio: KANGovern, KANComply, and KANGuard—a comprehensive suite built on the foundation of Microsoft Purview. These solutions work together to maintain data integrity , enforce compliance, and block unauthorized access across the full data lifecycle.
Whether it’s controlling shadow AI risks, ensuring regulatory readiness, or improving decision-making through clean, reliable data—Kanerika’s integrated approach delivers. Businesses can confidently embrace AI without compromising security or control. With Kanerika, data stays secure, usable, and in the right hands—every step of the way.
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Frequently Asked Questions What are the security risks of AI? AI security risks include adversarial attacks manipulating AI outputs, model poisoning during training, AI-powered cyberattacks, automated social engineering, and systems operating beyond human control. Additionally, proprietary AI models can be vulnerable to data extraction, and AI can inadvertently amplify biases leading to harmful outcomes.
What are the potential risks of AI in relation to data privacy? AI privacy risks include unauthorized data collection, model inversion attacks extracting training data, re-identification of anonymized information, excessive data retention, opaque processing without consent, surveillance capabilities, and algorithmic discrimination. Large language models may memorize and reproduce sensitive personal information from training datasets.
What is Microsoft Purview used for? Microsoft Purview is a unified data governance service that helps organizations manage and govern on-premises, multi-cloud, and SaaS data. It enables data discovery, classification, catalog creation, lineage tracking, access management, and compliance monitoring across an enterprise’s entire data estate.
Does Microsoft Purview use AI? Yes, Microsoft Purview incorporates AI and machine learning technologies. It uses AI for automatic data discovery, sensitive data classification, pattern recognition, anomaly detection, and providing intelligent insights. AI enhances Purview’s capabilities to understand, categorize, and protect data at scale .
What is Microsoft Purview AI classification? Microsoft Purview AI classification automatically identifies and labels sensitive information using pre-built or custom trainable classifiers. It employs machine learning to recognize patterns beyond simple keywords, detecting sensitive content like harassment, profanity, resumes, source code, and financial documents across organizational data.
What is data security posture management? Data Security Posture Management (DSPM) is a framework for continuously identifying , assessing, and remediating data security risks across an organization’s environments. It provides visibility into sensitive data locations, access patterns, security configurations, and compliance gaps while automating security controls.
What is Microsoft Purview DLP? Microsoft Purview Data Loss Prevention (DLP) identifies , monitors , and protects sensitive information across Microsoft 365 services, on-premises environments, and third-party services. It prevents accidental sharing of sensitive data through policy enforcement, content inspection, and automated protection actions to maintain compliance.
