AI Data Security Risks & DLP for AI

Overview of AI and Data Security

AI data security is not a single tool. It is a control system that identifies sensitive data, reduces what models can see, and enforces policy in real time at the edges where loss happens.

• Scope covers datasets, prompts, retrieved context, outputs, logs, and connected tools.
• Goals are simple: always know where sensitive data is, stop it from leaking, and prove control to auditors.
• Success depends on making security usable for builders, not just enforceable for security teams.

What Is AI Data Security?

Think of AI data security as the union of DSPM, DLP, and runtime guardrails for models and apps. It prevents data misuse and model abuse without breaking productivity.

• Discovery finds PII, PHI, PCI, secrets, source code, and regulated content.
• Prevention redacts, tokenizes, blocks, or revokes in place.
• Governance documents who used what data, when, and why, with evidence.

Role of Data in AI

Models inherit the quality and sensitivity of their data. Training sets shape behavior. Validation sets confirm generalization. Inference data carries live business context.

• If training data is dirty, you ship brittle or biased behavior.
• If inference data is overshared, you expose customer trust and IP.
• If logs are over-retained, you build a breach time capsule.

Why Secure AI Data?

The biggest risks now sit outside the traditional perimeter. A paste into a prompt can be as dangerous as a misconfigured bucket.

• Protect reputation and revenue by minimizing blast radius.
• Reduce regulator and customer risk with audit-ready controls.
• Enable innovation by making the safe path the easy path.

AI data security watchdogs are becoming vigilant. If you are a business dealing with sensitive customer data in generative AI apps, you cannot escape the scrutiny of regulatory bodies.

While Generative AI presents an exciting frontier, promising to transform the way we work, the risks follow the rewards. What’s scary is no AI vendor is ready to fully disclose if they are absolutely compliant with the latest regulations and if their customers have to face data security risks later.

This blog post aims to present the challenges posed by popular AI tools and how organizations can tackle AI data security risks head-on.

Top 5 AI Tools and Their Data Security Risks

In 2022, a shocking percentage of Americans fell prey to internet scams, resulting in a loss of nearly $10.3 billion. That’s the magnitude of havoc AI automation tools with “done for you” services can wreak. If they are free, they eventually come at a higher cost: business security and potential data loss for your customers.

Let’s review the top 5 popular AI tools and their data security risks you should know.

1. ChatGPT

In a shocking report by Gizmodo, ChatGPT-4 faked visual impairment to manipulate a human into solving a CAPTCHA puzzle and bypass a security test. An alarming example is that AI tools are good at deceit too.

Here are a few more AI security risks:

Hackers can misuse ChatGPT to generate sophisticated malware codes. ChatGPT can be manipulated into writing phishing emails that appear authentic and have the potential to steal user data. ChatGPT plug-ins could be exploited to steal users' chat histories, extract personal information, and execute malicious codes on remote devices.

The chatbot’s March 20th outage exposing the payment-related and other sensitive information of 1.2% of subscribers is shocking proof of its data security loopholes.  

Related read: Secure Every ChatGPT Interaction with Strac ChatGPT DLP

2. Google’s Bard chatbot

When Google launched its Bard chatbot, the news fueled concerns about data security and misinformation. And the predictions came sooner than expected.

Bard presented the following risks

1. Bard is trained on data from the internet. Like every AI model based on text scraped from the internet, Bard is prone to picking up on gender bias, racial discrimination, and controversial/hateful messaging.
2. Hackers can tap into vulnerabilities to exploit Bard and its training data. For example, they can trigger backdoor attacks, where a code can be hidden in the training model to sabotage the output and steal user data.
3. Non-compliance with the latest regulations like GDPR

Bard is trained on data from the internet. Like every AI model based on text scraped from the internet, Bard is prone to picking up on gender bias, racial discrimination, and controversial/hateful messaging.

Hackers can tap into vulnerabilities to exploit Bard and its training data. For example, they can trigger backdoor attacks, where a code can be hidden in the training model to sabotage the output and steal user data.

Non-compliance with the latest regulations like GDPR

Must read: Secure Your Gmail from Data Loss & Unauthorized Access

3. Zendesk chatbot

Next in line are Zendesk customer chatbots. Given the volume of data flowing through Zendesk every day, the following risks are unavoidable:

• App and system integrations may lead to data loss and unauthorized access unless monitored at a granular level.
• Links to files and attachments can be directly downloaded without authentication in Zendesk.
• Customized user interfaces can cause accidental leakage of sensitive data.

4. JIRA service desk chatbot

JIRA Align, the latest addition to the wide suite of cloud services under Atlassian, has received backlashes due to potential vulnerabilities and malware risks. Interestingly, after the vulnerabilities were addressed, the attackers could still obtain elevated privileges, extract Atlassian cloud credentials and potentially infiltrate Atlassian infrastructure.

5. Zoom AI companion

Zoom has been on the radar of regulatory bodies, mainly due to its long rap-sheet of data privacy and security concerns. Zoom AI companion, a generative AI assistant, was released to amp up productivity. However, given the company’s data collection practices in the past, customers are worried about the following:

• Hidden clauses to extract personal data for training AI models
• False promises of end-to-end encryption that may lead to Zoom-bombing intrusion by bad actors (similar to Zoom’s 2021 data security fiasco).
• Non-adherence to data privacy regulations and misuse of “service-generated data” for training purpose

Related read: Generative AI: Explained, Data Loss Risks, & Safety Measures

Ways DLP solutions can combat AI security risks

Despite all the drawbacks, generative AI tools are here to stay. Businesses need to deploy the best security measures to stay a few steps ahead of cybercriminals, and here’s how.

1. IP Leak Prevention

Samsung never imagined its trade secrets would be in the hands of OpenAI. The mishap occurred when Samsung employees mistakenly keyed in classified data such as source code for a new program, into ChatGPT. Now, ChatGPT retains any kind of data to train itself further. This implies that the entire world now has access to what was supposed to be the company’s confidential, proprietary data.

This raises the concern surrounding IP leakage and confidentiality when using generative AI. While companies can issue thousands of data usage policies and train employees on customer data hygiene, securing high-risk data at the source is the first step. Doesn’t matter where your data flows; masking sensitive data (e.g., your IP address) and encrypting the data in transit helps mitigate data security risks posed by AI.

The Strac Advantage:

Strac’s Data Loss Prevention (DLP) capabilities eliminate the leakage of IP data from SaaS and AI apps by scanning (discovering), classifying and remediating sensitive IP data, such as confidential documents, code, over AI websites like ChatGPT, Google Bard, Microsoft Copilot, and more. Also, Strac DLP protects LLM apps. See more: https://docs.strac.io/#operation/outboundProxyRedact

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2. PII PCI PHI Sensitive Data Leakage

Companies are worried about sensitive or confidential data being leaked to ChatGPT or any other AI site like grok, google bard.

The Strac Advantage:

Strac offers detection and remediation features like Blocking, Alerting, Redaction to protect sensitive data shared in text or files to any AI website. You can also configure custom policies on:

• What data elements to redact
• When to remediate
• Who should be allowed access.
• Create audit reports

3. Tokenize or Pseudonymize Sensitive Data and Send to AI website or LLM provider

It is common to have PII, PCI, PHI or any confidential data accidentally sent to any AI site. With Strac's Tokenization and Pseduonymization technology, Strac can automatically detect and tokenize sensitive data, insert the tokens into prompt, send the prompt containing tokens to AI websites or LLM. Strac also gives the option to toggle between tokenized data and real sensitive data if the user wants to see on the ChatGPT or any AI website. See example below.

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Strac’s AI Data Security Solutions

Strac operationalizes AI data security without agents. You get discovery, prevention, and governance that work where users already are.

Data Discovery and Classification

Find sensitive data across SaaS, cloud, and AI workflows automatically.

• Detect PII, PHI, PCI, secrets, and code in text, images, and PDFs.
• Use OCR to catch leaks inside screenshots, dashboards, and code editors.
• Build a live map of data exposure and risk.

Data Masking and DLP for AI

Enforce policy at the prompt boundary and inside the tools teams use.

• Redact sensitive fields in prompts and responses before the model sees them.
• Tokenize identifiers and keep the reversible map secure.
• Block risky uploads and auto-revoke overshared links where the data lives.
• Deploy through browser, API, and native integrations to move fast.

AI Security Posture Management

See how data moves through prompts, retrieval, and connected apps, then fix issues in place.

• Detect oversharing, overscoped access, and anomalous usage.
• Remediate automatically and record evidence for audit.
• Improve the metrics that matter: fewer incidents and faster fixes.

DLP for ChatGPT or any AI Site

Checkout Strac DLP for ChatGPT. Also, Strac DLP for Chrome Extension that will cover ANY website

DLP for LLM API

Checkout Strac API to automatically block/redact sensitive data when sent to LLM API like OpenAI, AWS Bedrock, and more.

In Summary

AI creates leverage when data is safe. The winning pattern is consistent. Discover and classify everywhere. Minimize what models see. Enforce policy at the edges. Monitor for drift and abuse. Measure performance, not configuration. Strac exists to make this pattern fast to deploy and easy to prove, so your teams can innovate with confidence and your auditors can sleep at night.

New Section 🌶️Spicy FAQs

1) How is AI actually used in data security today?

AI turns noisy telemetry into precise actions that reduce risk without slowing teams. It connects patterns across endpoints, networks, SaaS, and AI tools to spot misuse before it becomes a breach.

What this looks like in practice:

• Threat detection that stitches together small signals into one clear incident story.
• UEBA that flags unusual data pulls, risky file shares, and odd prompt patterns.
• Content understanding that classifies sensitive data in real time and decides whether to allow, redact, tokenize, or block.
• SOAR-style recommendations that propose next best actions and automate safe fixes.

Where Strac adds value:

• Agentless classification across SaaS, cloud, and AI workflows including text, images, and PDFs with OCR.
• Real-time decisions at the prompt boundary, so sensitive fields are controlled before the model ever sees them.
• Automated remediation in place, like revoking a public link or tightening access on the source system, not just raising an alert.

2) Is my data safe with AI tools like ChatGPT, Gemini, and Copilot?

It can be safe if you minimize what the model sees and enforce policy where users actually work. Safety is not a checkbox. It is a set of guardrails before, during, and after each interaction.

What “safe” means for AI use:

• Before: prompts and file uploads are checked and sensitive fields are masked or tokenized.
• During: only approved plugins and domains are allowed and requests are rate limited.
• After: outputs and logs are scrubbed and retained only as long as needed for audit.

Where Strac adds value:

• Policy enforcement inside the browser and via API for ChatGPT, Gemini, Copilot, and more.
• Tokenization that keeps workflows useful while preventing raw identifiers from reaching the model.
• Short, audit-ready evidence of what was detected and what action was taken, so trust and compliance move together.

3) What are the three types of data security and how do they apply to AI?

You still need physical, administrative, and technical controls. AI changes where and how you apply them, not whether you need them.

How the pillars translate to AI:

• Physical: protect keys and secure build environments that produce model artifacts and token vaults.
• Administrative: define acceptable AI use, approval gates for datasets and plugins, and vendor policies that match your risk.
• Technical: enforce DLP at the prompt boundary, maintain DSPM visibility across SaaS and storage, and keep least privilege on identities and model tools.

Where Strac adds value:

• Continuous DSPM mapping that shows where sensitive data actually is across your SaaS and cloud.
• DLP that operates in real time on prompts, responses, and file flows, with OCR to catch leaks hiding in screenshots and dashboards.
• Built-in playbooks that combine policy, people, and platform into one measurable operating model.

4) How do I protect my data from AI without blocking productivity?

Make the safe path the easy path. Put controls where users already work and automate the fixes that used to take tickets and time.

Five moves that work:

1. Turn on discovery across your top SaaS and storage to baseline exposure and public links.
2. Enforce redaction and tokenization for PII, PHI, PCI, secrets, and code in prompts and uploads.
3. Block risky uploads to unapproved AI tools and auto-revoke overshared files in place.
4. Allowlist approved AI domains and model tools and alert on everything else.
5. Keep only what you must for audit and keep it for as little time as possible.

Where Strac adds value:

• One-click policies that block exfil to AI domains, redact sensitive fields, and revoke links automatically.
• Fast, agentless rollout across browser and native integrations, so teams keep shipping while risk drops.
• Metrics that matter out of the box, including time to detect, time to fix, and exposure trending down.

5) Which AI is best for cybersecurity?

There is no single best. Choose by job, then layer capabilities that complement each other and measure outcomes, not features.

How to choose with confidence:

• For real-time detection, use engines tuned for behavior on endpoints and networks.
• For insider and SaaS risk, apply UEBA that understands sharing, access, and prompt patterns.
• For phishing and content risk, use NLP classification that learns from your feedback.
• For AI safety, control prompts, uploads, outputs, and tool actions with policy-aware guardrails.
• For orchestration, use automation that executes fixes in the systems where data lives.

Where Strac adds value:

• A unified layer that discovers sensitive data, enforces policy at AI touchpoints, and remediates in the source systems.
• OCR-powered detection that finds leaks others miss, like sensitive data in screenshots and exported dashboards.
• Evidence and reporting that prove control to auditors and boards while freeing teams to move faster.

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