ChatGPT Security Risk and Concerns in Enterprise

Alan Turing, a visionary in the 19th century, predicted the potential for machines to alter their instructions and learn from experience. This laid the foundation for what we know today as Artificial Intelligence (AI). AI plays a significant role in our daily lives - from Siri's assistance to facial recognition technology that unlocks our phones. It has seen immense growth, with major milestones such as the launch of ChatGPT in November 2022 and subsequent releases of GPT-4 and ChatGPT plugins. As organizations increasingly rely on generative AI, ChatGPT Security Risks have become a growing concern, demanding stronger governance and data protection measures.

As the use of AI continues to grow, a McKinsey survey has revealed a concerning trend - only 21% of companies have established policies for its use. Surprisingly, inaccuracies in AI are now becoming a bigger concern than cybersecurity, with just 32% of firms addressing this risk. This calls for a detailed examination of AI technologies such as ChatGPT and their impact on enterprise security.

What is ChatGPT Security?

ChatGPT security refers to the policies, technologies, and practices that protect data processed, shared, or generated through ChatGPT. In 2025, as the enterprise adoption of large language models (LLMs) continues to grow, understanding ChatGPT security is critical for safeguarding sensitive data and ensuring compliance across regulated industries.

At its core, ChatGPT security covers three main pillars; data protection, misuse prevention, and user privacy. This includes controlling what data enters the model, how it’s transmitted and stored, and ensuring that interactions remain compliant with standards such as GDPR, HIPAA, and SOC 2.

OpenAI has introduced several security measures for ChatGPT, including encrypted data transmission (TLS 1.2+), secure infrastructure within Microsoft Azure, and enterprise-grade access control. Moreover, ChatGPT Enterprise and API users can opt for zero-data retention (ZDR), ensuring that prompts and responses are not stored or used to train future models.

For organizations deploying ChatGPT across departments, understanding these fundamentals of ChatGPT security isn’t optional — it’s the foundation of responsible AI governance. Businesses that fail to establish strict controls around model use risk data leakage, compliance violations, and reputational harm, making it imperative to pair AI innovation with enterprise-grade data loss prevention (DLP).

Are ChatGPT Security Risks a Major Threat to Businesses in 2026?

ChatGPT Security Risks are no longer theoretical; in 2026 they are an operational reality for most enterprises adopting generative AI. As organizations increasingly embed ChatGPT into customer support, code generation, analytics, and sales workflows, sensitive data exposure through prompts, responses, and integrations has grown exponentially. Misconfigured plug-ins, employee misuse, and a lack of real-time data monitoring now represent the top three AI-related security incidents reported by CISOs.

Real-world breaches have shown how AI prompts can unintentionally capture PII, PHI, or source code and store them within model memory or logs, violating GDPR, HIPAA, and SOC-2 mandates. Traditional DLP tools cannot detect or redact these risks across AI workflows, leaving businesses vulnerable to compliance fines and reputational damage.

That’s where Strac’s AI-aware DLP and DSPM coverage becomes critical. Strac protects data across SaaS, Cloud, Endpoint, and GenAI surfaces — including ChatGPT. Using ML and OCR-based detection, Strac automatically discovers and redacts sensitive data before it leaves the organization, ensuring that AI usage remains compliant and secure. With real-time visibility, inline redaction, and zero-agent deployment, enterprises gain a balance of innovation and control over their generative AI adoption.

✨Common ChatGPT Security Risks

1. Asking Chat GPT for credentials

ChatGPT is a chatbot powered by the vast Common Crawl dataset, which includes a variety of internet text sources such as GitHub repositories. This dataset is known for its large size and scope but may also contain embedded sensitive information like API keys. While ChatGPT is designed to avoid reproducing this type of information, there is a concern that it may unintentionally mimic genuine credentials or replicate real keys from its training data. 

2. Lack of proper context

ChatGPT's performance is based on factors such as the training data quality and model architecture. These models are trained on large amounts of text data that may contain biases. As a result, the generated outputs may also contain biased or unfair results, particularly if the training data is not diverse. 

Also read: Does ChatGPT save your data?

3. Security and data leakage

ChatGPT poses a serious risk to companies by potentially exposing sensitive information to others. This has already impacted major organizations like Amazon and Samsung. Research from Cyberhaven revealed that, in just one week, employees at an average of 100,000 people at a company entered confidential data into ChatGPT nearly 200 times. The platform's security flaws have allowed thousands of employees to attempt inputting customer data, source codes, and other regulated data. In fact, a bug in the platform resulted in personal and billing data being accidentally leaked. 

4. Confidentiality and privacy risks

ChatGPT can gather personally identifiable information (PII) from interactions for response generation. OpenAI's privacy policy specifies that this encompasses user account specifics, conversation content, and web interaction data. If unauthorized individuals were to gain access, this information could be exposed, potentially compromising user data, training prediction data, and details regarding the model's architecture and parameters.

In fact, ChatGPT experienced a disruption caused by a bug in Redis, exposing sensitive user information, including chat titles, chat history, and payment details. In addition to conventional attacks, ChatGPT faces the threat of prompt injection and model poisoning, potentially modifying the security and performance without the user being aware.

5. Intellectual property concerns

The ownership and copyright implications of code or text produced by ChatGPT can be complex. When Samsung engineers input proprietary source code into ChatGPT, they allow this sensitive information to influence the AI's future outputs. This poses a risk of inadvertent disclosure to competitors or threat actors. 

In another instance, a Samsung executive used ChatGPT to convert internal meeting notes into a presentation; there is a risk that someone from a competing company could use ChatGPT to inquire about Samsung's business strategy, potentially compromising sensitive data.

6. Data reconstruction

AI models face a serious privacy threat from data reconstruction attacks, which use methods like model inversion to extract sensitive information from training data. This can include confidential details like biometrics or medical records. Model theft can further amplify this risk by replicating the model's training set and exposing confidential information during the training process.

7. Data poisoning

AI models like ChatGPT are at risk of data poisoning attacks, where harmful data can be injected or training data labels can be altered, potentially impacting their performance. Tampering with ChatGPT's training sources, updates from external databases, or user conversations could result in errors or manipulated behaviors. Furthermore, the model's dependence on user feedback for optimization could be exploited to degrade its performance.

8. Sponge Sampling

Sponge samples are a new AI security threat similar to denial of service (DoS) attacks, increasing model latency and energy use, thus straining the hardware and disrupting machine learning model availability. A study by Shumailov et al. employed sponge samples to prolong the response time of the Windows Azure translator from 1 millisecond to 6 seconds, significantly affecting language models.

9. Risk of exposure through plugins

Although OpenAI reviews plugins to ensure they align with our content, brand, and security policies, it's important to note that when using plugins, your data is still being sent to third-party entities separate from OpenAI. These third parties have their own data processing policies. Furthermore, using plugins may expose ChatGPT to new vulnerabilities or potential cyber-attacks to access end-user data.

Potential Misuses of ChatGPT

1. AI phishing emails

Cybercriminals have used their abilities to create convincing phishing schemes, including realistic emails and fake landing pages. These tactics have been effective in general phishing attacks and more targeted man-in-the-middle (MitM) attacks. This technology also enables scammers to impersonate organizations or individuals convincingly. A report by WithSecure highlights experiments where ChatGPT was used to craft deceptive phishing emails to transfer money to scammers.

2. Malware

ChatGPT, despite its protective measures, is susceptible to users discovering ways to bypass restrictions and misuse its AI capabilities for harmful purposes. A Checkpoint research report has revealed that threat actors in underground hacking forums already use OpenAI tools to create malware. Additionally, a study by Recorded Future has shown that even threat actors with limited programming skills can harness ChatGPT to enhance existing malicious scripts, making them harder for threat detection systems to spot. 

ChatGPT can be misused to develop complex encryption tools in Python, provide guidance on creating dark web marketplace scripts, generate unique email content for business email compromise (BEC) attacks, expedite the creation of malware for crime-as-a-service operations, and produce large volumes of spam messages to disrupt communication networks.

3. API attacks

APIs have become increasingly popular in enterprises, but unfortunately, so have API attacks. Salt Security researchers have reported an 874% increase in unique attackers targeting customers' APIs in just six months of 2022. In fact, in the first quarter of 2023, there was a significant 400% increase in attackers targeting APIs compared to the previous year. Cybercriminals are now using generative AI to quickly identify vulnerabilities in APIs, a task that used to be time-consuming to analyze API documentation, gather data, and create queries at a faster rate. 

Best Practices to Secure Sensitive and Confidential PII ,PHI in ChatGPT

Across the globe, employees have embraced ChatGPT as a valuable tool for automating various tasks. They utilize it to input proprietary data and generate initial drafts for code, marketing materials, sales presentations, and business plans.

To ensure the safe and efficient use of ChatGPT in these contexts, here are some best practices to consider:

1. Use ChatGPT responsibly

While ChatGPT offers a convenient way to communicate and gather information, there are potential risks associated with sharing sensitive information. It is important to be cautious when using this chatbot and consider its limitations, which could lead to inaccurate responses. Therefore, it is advised not to use ChatGPT for critical matters. To ensure the security of your conversations on ChatGPT, experts recommend disabling chat history and model training in your account settings.

2. Implement data governance policies

Establish strong data governance policies that clearly outline how data is classified, protected, and shared within the organization. This includes setting guidelines for handling sensitive information in AI chatbot conversations, implementing access controls to restrict entry into AI chatbot systems, and restricting extensions and applications. Multi-factor authentication (MFA) can also provide extra security for all your accounts.

3. Employee training

Educate employees on the potential risks and responsibilities of using AI chatbots ethically. This training should cover technical aspects and incorporate these tools with existing company policies and processes. Employees must have a comprehensive understanding of how AI tools function and what their role is in the organization. This involves transparently explaining these tools' functioning, data usage, and output application.

4. Network Detection and Response platform 

An NDR platform offers comprehensive cybersecurity monitoring to safeguard your network against unauthorized access by malicious threat actors. Effective NDR solutions use AI and ML to identify and prevent unauthorized access, while a well-maintained zero-trust environment further strengthens defenses by restricting access to authenticated users.

5. Create strong passwords

A strong password should be unique and regularly updated. Avoid repeating passwords for your accounts on different platforms and apps to prevent password-stuffing attacks.

6. Patches and firewall

Make sure to install the latest updates and patches available. Enable your operating system's firewall and activate your router's firewall for additional protection. To further secure your data and location, consider using a private VPN that encrypts your information.

7. Regular monitoring

Add extra layers of protection to your accounts and ensure all alerts are activated. Watch out for any unusual patterns in chatbot usage and set up alerts for potential data breaches. Strac monitors all the data sent to ChatGPT and enforces rules to prevent sharing sensitive information.

✨Security Concerns in Third-Party Integration of ChatGPT

One of the most underestimated security risks of ChatGPT arises when it’s integrated with third-party platforms such as CRMs, ticketing systems, or communication tools like Slack and Jira. Each integration extends ChatGPT’s reach; but also its attack surface.

When businesses connect ChatGPT through APIs to tools containing PII, PHI, or financial data, several vulnerabilities can emerge. These include data interception during transmission, weak API authentication, or even inadvertent data exposure if the integration lacks strict access controls. According to NordLayer’s analysis on ChatGPT security risks, misconfigured or unsecured API channels can allow sensitive information to travel unencrypted, making it susceptible to man-in-the-middle attacks.

To mitigate these integration risks, organizations should:

• Encrypt all data in transit using TLS or HTTPS across every integration point.
• Apply granular access controls that limit ChatGPT’s data exposure to only necessary fields or datasets.
• Use secure OAuth-based authentication to connect ChatGPT with internal systems.
• Monitor integration activity logs continuously for anomalous API calls or unauthorized access attempts.

Industry-specific examples highlight the risks: A marketing CRM linked to ChatGPT could inadvertently share client PII through training prompts, while a customer support integration might expose confidential tickets containing PHI. By embedding Strac’s agentless DLP and redaction capabilities, these interactions can be automatically scanned and sanitized before any sensitive data reaches ChatGPT.

Real-World Examples of ChatGPT-Related Threats

Despite ChatGPT’s rapid enterprise adoption, several real-world security incidents have highlighted its potential vulnerabilities — emphasizing why AI data protection must evolve alongside innovation.

One of the earliest cases involved Samsung engineers accidentally pasting proprietary source code into ChatGPT while debugging an issue, inadvertently sharing sensitive intellectual property with an external model. In another, over 225,000 stolen ChatGPT credentials surfaced on dark web marketplaces due to infostealer malware compromising employee devices — exposing internal business conversations and data.

According to NordLayer’s 2025 review, other threats include prompt injection attacks, where malicious users trick the model into revealing or altering hidden instructions, and data leakage through AI-enhanced phishing campaigns. These aren’t theoretical; they’ve already been used to exfiltrate sensitive information or generate convincing fake content for social engineering.

Each of these examples demonstrates that ChatGPT-related threats don’t always stem from the model itself; but often from human error, lack of policy enforcement, or insufficient DLP integration. To prevent recurrence, enterprises should:

• Implement zero-data-retention policies when using ChatGPT APIs or enterprise versions.
• Educate teams on safe prompt practices, especially avoiding inclusion of client or internal data.
• Integrate continuous monitoring via tools like Strac that identify and remediate sensitive data exposure across SaaS, GenAI, and endpoint environments.

When properly governed, ChatGPT can become a productivity multiplier; but without these safeguards, it remains a potential compliance and reputational risk waiting to happen.

Where Is AI Security Heading in 2026 and Beyond?

AI Security is shifting from static protection to proactive, continuous governance. As LLMs integrate into enterprise software and employee workflows, organizations will need dynamic, context-aware security that can adapt to the unpredictable nature of AI interactions. The next phase of protection goes beyond network monitoring — it centers on data-centric control across all AI endpoints and connected SaaS tools.

By 2026 and beyond, expect the convergence of DSPM (Data Security Posture Management) and AI-driven DLP, enabling organizations to automatically discover, classify, and remediate sensitive information in real time. Security tools must learn and act autonomously, flagging high-risk data movement between AI agents, internal databases, and third-party integrations.

Strac is already shaping this future. With its agentless architecture, unified SaaS-Cloud-GenAI coverage, and advanced ML/OCR detection, Strac ensures that AI innovation doesn’t come at the cost of security. The platform bridges compliance and productivity — providing CISOs and IT teams full visibility, automated redaction, and scalable data protection across every digital surface.

Bottom Line:

ChatGPT Security Risks are evolving in step with enterprise AI adoption. In 2026 and beyond, safeguarding data across GenAI tools demands solutions that merge precision detection, real-time redaction, and compliance-ready coverage. Strac delivers exactly that; a unified DSPM + DLP platform built for the AI era, keeping businesses secure while empowering innovation.

✨Automate Sensitive Scanning, Classification and Redaction in ChatGPT

Strac ChatGPT DLP uses 'Automated Sensitivity Analysis' to continuously monitor and classify ChatGPT content, ensuring the protection of sensitive data such as PII, PHI, and other confidential information. To safeguard user privacy, Strac masks any sensitive parts of conversations and only grants access to authorized personnel when needed. With Strac, users have the ability to set their own rules for data sensitivity in ChatGPT interactions, providing a sense of security and control over their information.

Protection against accidental shares

Mistakes happen. Recognizing this, Strac ensures that unintentional data disclosures during ChatGPT interactions are mitigated, safeguarding internal information that employees might unknowingly expose, thinking they're in a secure space.

Real-time data anonymization

Strac promptly anonymizes Personally Identifiable Information (PII) and Payment Card Information (PCI) within ChatGPT prompts, certifying that proprietary data remains undisclosed and unshared with ChatGPT.

Compliance assurance

Strac's solution ensures that interactions with ChatGPT comply with stringent privacy regulations, like GDPR and CCPA, by anonymizing sensitive information before it reaches ChatGPT, thus protecting businesses from potential non-compliance penalties.

Chrome extension for seamless integration

Strac offers a secure Browser Extension (Chrome, Edge, Safari, Firefox) that enables businesses to harness the capabilities of ChatGPT without compromising on data security standards, presenting a balanced blend of functionality and safety.

Strac offers pre-designed compliance setups specifically for ChatGPT, making meeting standards such as PCI, HIPAA, and GDPR easy. With detailed interaction audits and up-to-date security insights, Strac simplifies the auditing process and helps you stay ahead of emerging threats. 

Conclusion: Strengthening AI Adoption by Managing ChatGPT Security Risks

As businesses embrace generative AI, understanding and managing the security risks of ChatGPT becomes essential to responsible innovation. Every interaction, integration, and prompt holds the potential to expose sensitive data — which is why aligning enterprise policies with OpenAI’s built-in protections is critical. By combining encryption, Zero Data Retention, and robust governance frameworks, organizations can minimize the security risks of ChatGPT while still benefiting from its transformative power.

However, true protection comes from going beyond native features. Implementing continuous monitoring, access control, and intelligent redaction through tools like Strac ensures that AI adoption remains both compliant and secure. In 2025 and beyond, those who proactively address the security risks of ChatGPT will not only protect their data but also build lasting trust in how AI is used across their enterprise.

🌶️Spicy FAQs on ChatGPT Security Risks

Are there security risks using ChatGPT?

Yes — even though ChatGPT is built with strong protections, there are security risks of ChatGPT that every organization should understand. Risks emerge when employees unintentionally share sensitive data, when integrations are misconfigured, or when access controls are too broad.

OpenAI encrypts all data in transit (TLS 1.2+) and at rest (AES-256), while ChatGPT Enterprise and ChatGPT Edu allow admins to manage retention and apply Zero Data Retention (ZDR) on eligible API workloads. These features greatly minimize exposure — but they don’t replace governance.

Businesses that use ChatGPT responsibly establish AI usage policies, DLP tools, and user training to keep confidential data out of prompts and ensure compliance with frameworks like SOC 2 and GDPR. Understanding and mitigating the security risks of ChatGPT is what separates secure innovation from accidental data leakage.

What are the common security risks associated with using ChatGPT?

The most common security risks associated with ChatGPT stem from how humans and systems interact with it — not from the model itself. These include:

• Accidental data leaks: Copying and pasting PII, PHI, or IP into ChatGPT without retention controls.
• Weak access management: Shared accounts or lack of SSO make it easier for misuse to occur.
• Limited visibility: Without proper logs or DLP integration, organizations can’t track sensitive data flows.

OpenAI mitigates many of these risks through encryption, Zero Data Retention, and enterprise-level security certifications. However, companies should still reinforce these defenses by setting clear usage policies, monitoring prompts, and enforcing strict admin oversight.

Managing the security risks of ChatGPT means pairing OpenAI’s built-in protections with proactive organizational controls that prevent human error.

How can third-party integrations affect ChatGPT security?

When ChatGPT is integrated with tools like CRMs, helpdesks, or file-sharing apps, new ChatGPT security risks can surface. Each connected system becomes a potential gateway for unauthorized access or data mishandling.

OpenAI supports enterprise-grade controls such as SSO, RBAC (role-based access control), and customer-managed encryption keys through Enterprise Key Management (EKM). These help secure integrated deployments, but organizations must also ensure that API connections are encrypted, scopes are limited, and retention policies align across all systems.

By combining OpenAI’s encryption and retention features with continuous monitoring, companies can maintain ChatGPT security across every integration — ensuring that no sensitive data travels where it shouldn’t.

How does ChatGPT handle data encryption?

ChatGPT security relies heavily on robust data encryption to protect users and organizations. OpenAI encrypts all data in transit using TLS 1.2+ and at rest using AES-256, ensuring conversations, prompts, and API exchanges stay secure.

For enterprises, ChatGPT Enterprise and Edu add additional layers — such as Enterprise Key Management (EKM), which allows businesses to use their own encryption keys and maintain control over access. These protections align with OpenAI’s SOC 2 Type II and GDPR-compliant infrastructure, giving organizations full confidence in the platform’s encryption standards.

Understanding how ChatGPT encryption works is essential to addressing the security risks of ChatGPT. Encryption ensures your data stays private, but governance, access control, and DLP protection ensure it stays compliant and contained.