ChatGPT Enterprise Security: Risks & Best Practices

What is ChatGPT Enterprise Security?

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ChatGPT Enterprise Security is the built-in framework that protects organizational data while governing how employees use the platform. It includes encryption of data in transit and at rest, enterprise-level authentication such as SAML SSO, role-based access controls, and an admin console for user management and usage insights. It also provides compliance with standards like SOC 2 and GDPR, ensures that business data is not used to train models, and gives enterprises ownership and control over both inputs and outputs.

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Key ChatGPT Enterprise Security Risks

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Enterprises adopting ChatGPT face security risks that go beyond traditional SaaS concerns. Each ChatGPT security risk poses unique challenges for organizations aiming to adopt AI responsibly. The table below outlines the most significant threats, their nature, and the potential impact on organizational security and compliance:

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Risk	Description	Enterprise Impact
Data Leakage and Retention Issues	Prompts and outputs may contain sensitive data that, if mishandled or retained improperly, could be exposed through logs or integrations.	Loss of confidential business information, exposure of PII, and compliance violations.
Unauthorized Access and Account Takeover	Weak authentication or compromised credentials can allow attackers to hijack accounts and misuse enterprise ChatGPT access.	Data breaches, impersonation, and disruption of enterprise workflows.
Prompt Injection Attacks	Malicious prompts can manipulate the model into revealing data or executing unintended actions.	Unauthorized disclosure of internal data and circumvention of established controls.
Intellectual Property Exposure	Employees may share proprietary code, designs, or strategies in prompts, making sensitive IP vulnerable.	Competitive disadvantage and potential legal disputes.
AI Hallucinations Leading to Misinformation	The model may generate inaccurate or fabricated outputs that enterprise users mistakenly rely on.	Poor decision-making, reputational damage, and compliance risks.
Regulatory and Compliance Gaps	Inconsistent controls across AI interactions may result in non-compliance with GDPR, HIPAA, or industry standards.	Regulatory penalties, audits, and erosion of customer trust.

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Security Features of ChatGPT Enterprise

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ChatGPT Enterprise is designed with enterprise-grade security features that address organizational requirements for privacy, compliance, and operational oversight. These features work together to ensure safe use across teams while giving administrators full control over data and user interactions.

Encryption and Privacy Controls: All data is encrypted in transit using TLS 1.2+ and at rest with AES-256. Neither prompts nor outputs are used to train OpenAI’s models, ensuring enterprise data remains private by default. These controls ensure that sensitive information remains protected across the entire lifecycle of AI interactions.
Admin Console and Role-Based Access: The platform provides an admin console that allows enterprises to manage team members at scale. Role-based access controls, domain verification, and SSO with SAML give security teams the ability to enforce fine-grained permissions and user management policies.
Data Residency and Ownership: Organizations retain ownership of their inputs and outputs, with the ability to define how long data is stored. This control extends to connecting or restricting internal data sources, ensuring that enterprise data flows remain within approved boundaries.
Compliance Certifications (SOC 2, GDPR): ChatGPT Enterprise has achieved SOC 2 compliance and supports alignment with GDPR requirements. These certifications validate that the platform meets recognized standards for security, confidentiality, and data protection.
Usage Analytics and Monitoring: Administrators gain visibility into adoption and usage trends through built-in analytics. These insights help security teams monitor AI interactions, detect potential anomalies, and refine policies to improve enterprise security posture.

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Challenges of Securing ChatGPT in the Enterprise

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Even with enterprise-grade security features, organizations face challenges in managing how employees use ChatGPT at scale. Effective shadow AI discovery becomes critical here, as many risks stem from unapproved usage, data complexity, and inconsistent enforcement of security policies:

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Challenge	Description	Enterprise Impact
Shadow AI and Unauthorized Use	Employees may use unapproved ChatGPT accounts or free versions outside enterprise controls.	Data leaves managed environments, increasing the risk of leaks and non-compliance.
Limited Visibility Across SaaS Environments	AI interactions span multiple SaaS apps, but centralized monitoring is often lacking.	Security teams struggle to track sensitive prompts and outputs, reducing oversight.
Unstructured Data Risks and Classification Gaps	Because ChatGPT processes unstructured text, sensitive data may be hidden within prompts and escape proper classification.	Sensitive data exposure and compliance failures due to missed detection.
Inconsistent Access Policies	Variability in how teams apply authentication and authorization for AI tools.	Uneven protection across departments, leaving gaps that attackers can exploit.

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Best Practices for ChatGPT Enterprise Security

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Enterprises can reduce risks by implementing layered security measures that combine policies, automation, and ongoing oversight. The following practices represent the core steps for securing ChatGPT Enterprise in production environments:

Enforce Zero-Trust Security Models: Adopting a zero-trust approach means verifying every user and request, regardless of location or device. In ChatGPT Enterprise, this involves enabling SAML-based SSO, enforcing multi-factor authentication, and applying least-privilege role assignments through the admin console. These measures prevent unauthorized access and reduce the risk of compromised accounts being misused.
Limit Sharing of PII and Sensitive Data: Employees should be trained to avoid submitting personally identifiable information, intellectual property, or regulated business records into prompts. Even though ChatGPT Enterprise encrypts data and does not train on inputs, the safest strategy is to minimize exposure. Security teams should implement acceptable use policies and data handling guidelines tailored to AI interactions.
Automate Classification and Redaction: Sensitive information often appears in unstructured text that users feed into ChatGPT. Automated classification and redaction tools can detect patterns such as financial details, health records, or source code before prompts are sent. This automation reduces reliance on manual reviews and ensures consistent enforcement of data protection rules.
Review and Update Controls Regularly: AI adoption in the enterprise evolves quickly, and static controls become outdated. Security teams should regularly reassess authentication settings, retention policies, and monitoring configurations to align with updated regulatory guidance and enterprise security standards. Periodic reviews help close gaps that emerge as new AI features are deployed.
Audit AI Usage Continuously: Audit logs should record prompts, responses, and contextual metadata for every enterprise user session. Integrating these logs into existing SIEM or compliance monitoring tools enables security teams to investigate incidents, detect anomalies such as excessive data queries, and demonstrate compliance during audits. Continuous auditing turns AI usage from a blind spot into an accountable part of enterprise security operations.

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Insight by
Dr. Tal Shapira
Cofounder & CTO at Reco

Tal is the Cofounder & CTO of Reco. Tal has a Ph.D. from Tel Aviv University with a focus on deep learning, computer networks, and cybersecurity and he is the former head of the cybersecurity R&D group within the Israeli Prime Minister's Office. Tal is a member of the AI Controls Security Working Group with CSA.

Expert Tip: Reducing Data Exposure in ChatGPT Prompts

In my work with enterprise teams adopting generative AI, I’ve seen how easily sensitive information slips into prompts when employees are under pressure to get fast results. To reduce this risk, focus on three practical steps:

Educate Users Early: Train teams to recognize sensitive data such as customer identifiers, financial details, and source code that should never be placed into AI prompts.
Apply Automated Checks: Deploy tools that detect and flag sensitive data in real time before prompts reach ChatGPT.
Review Patterns Regularly: Analyze AI interaction logs to identify recurring risky behaviors and adjust policies accordingly.

With the right balance of training and controls, enterprises can minimize data exposure while still allowing employees to benefit fully from AI tools.

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ChatGPT Security in SaaS Environments

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When ChatGPT Enterprise is connected to existing SaaS platforms, its security posture depends on how data flows between collaboration tools, file repositories, and AI systems. The following areas highlight where risks and controls matter most.

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ChatGPT Plugins in Collaboration Tools

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Plugins extend ChatGPT into collaboration platforms like Microsoft Teams, but each integration also expands the enterprise attack surface. Each plugin introduces new permission scopes, API calls, and data exchange paths. Without proper oversight, sensitive data may move outside approved environments, or plugins could be exploited for prompt injection attacks. To mitigate these risks, enterprises should maintain an allowlist of approved plugins, enforce least-privilege access scopes, and log all plugin activity into enterprise audit systems.

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AI Risks in Google Drive, Slack, Notion

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File-sharing and messaging platforms are common entry points for AI adoption, and they create distinct security challenges:

Google Drive: Sensitive documents shared through AI prompts may bypass established classification or retention policies. Improper link sharing or misconfigured permissions can expose regulated data.
Slack: Prompts and outputs exchanged in channels or direct messages can include confidential business details. Third-party apps installed in workspaces may expand access without administrator visibility.
Notion: Knowledge bases often store proprietary designs and strategy documents. If linked to AI prompts, this data can be exposed unintentionally to broader audiences.

Across these platforms, consistent monitoring, classification, and access controls are required to keep AI interactions aligned with enterprise security policies.

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Enforcing Policies Across Apps with One Control Plane

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Managing AI risks in multiple SaaS environments becomes more effective when organizations adopt a single control plane. By centralizing identity through SAML SSO and integrating data classification policies across apps, enterprises can apply uniform rules for retention, auditing, and sensitive data handling.

Solutions like Microsoft Purview’s Data Security Posture Management for AI enable visibility into prompts and outputs across platforms, while existing SIEM or DLP tools can consolidate activity into a single monitoring workflow. This unified approach ensures that enterprise security policies remain consistent, even as ChatGPT Enterprise interacts with multiple SaaS applications.

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ChatGPT Enterprise Security Real-World Incidents

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Several incidents highlight how enterprise use of ChatGPT and similar AI tools can expose organizations to significant risks when controls are not in place.

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1. Samsung Data Leak

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In April 2023, Samsung employees unintentionally exposed confidential information by pasting source code, meeting transcripts, and test data into ChatGPT. The leaked material included proprietary semiconductor details, prompting the company to ban generative AI tools on internal networks and launch its own in-house solution. This incident illustrates how quickly sensitive data can leave a controlled environment when employees rely on AI for productivity.

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2. AI-Powered Phishing Attempts

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Attackers have begun using generative AI to craft convincing spear-phishing emails and chat messages. These campaigns exploit ChatGPT’s ability to generate natural-sounding text at scale, increasing the likelihood of employees falling for fraudulent requests. Enterprises that do not combine phishing-resistant MFA with user training face elevated risks of credential theft and account takeover.

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3. Fake Customer Support Bots

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Threat actors have impersonated customer service chatbots by deploying AI-powered systems designed to trick users into sharing login credentials or payment information. When these bots mimic the tone and branding of legitimate enterprises, they create reputational damage and erode customer trust. Without clear authentication and monitoring of customer-facing AI, enterprises risk brand exploitation and data theft.

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How Reco Strengthens ChatGPT Enterprise Security

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ChatGPT Enterprise offers native controls, but organizations often require deeper visibility into how AI interacts with their SaaS environment. Reco strengthens ChatGPT Enterprise security by detecting sensitive data, classifying risks, and enabling governance workflows that guide responsible AI usage.

Sensitive Data Detection in AI Prompts and Outputs: Reco identifies when sensitive information such as PII, financial records, or source code is shared through AI interactions. This detection helps enterprises maintain compliance and reduce the risk of inadvertent data exposure.
Automated Classification and Alerting of Sensitive Content: Reco automatically classifies detected content based on sensitivity levels and generates alerts for security teams. This ensures that high-risk interactions are flagged quickly without relying solely on manual reviews.
Continuous Visibility Across SaaS, Cloud, and AI Tools: Reco provides continuous visibility into data flows and user activity across SaaS, cloud platforms, and AI applications. Positioned as a modern SaaS security platform, it helps security teams understand how AI is being used and where sensitive data may be vulnerable.
Governance Workflows and Policy Recommendations: Reco supports governance by mapping high-risk activities to relevant compliance frameworks and providing targeted policy recommendations. Security teams can assign ownership, review flagged interactions, and align AI usage with enterprise security and regulatory requirements.

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Conclusion

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Securing ChatGPT Enterprise requires more than built-in protections. The risks of data leakage, unauthorized access, and regulatory misalignment demand continuous oversight and well-defined controls. OpenAI provides enterprise security features such as encryption, access management, and compliance certifications, while solutions like Microsoft Purview and Reco extend visibility, compliance alignment, and policy governance across SaaS and cloud environments.

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For CISOs and security teams, the priority is to monitor AI usage as closely as any other enterprise system, classify and protect sensitive data, and apply governance frameworks that ensure responsible adoption. When managed correctly, ChatGPT Enterprise can deliver productivity benefits without compromising enterprise security.

How can organizations prevent employees from sharing sensitive data with ChatGPT?

The most effective approach combines technical and organizational measures:

Deploy ChatGPT Enterprise with SAML SSO and role-based access to enforce secure authentication.
Monitor usage through tools such as Microsoft Purview DSPM for AI to capture prompts and responses.
Apply insider risk and communication compliance policies that detect risky AI interactions.
Reinforce security controls by establishing clear acceptable use guidelines and providing ongoing employee training.

Explore how Reco’s ChatGPT security risk guidance can help enterprises implement these safeguards across SaaS and AI environments.

Does ChatGPT Enterprise store or learn from user prompts?

No. OpenAI does not train its models on business data from ChatGPT Enterprise by default. Enterprises also control how long data is retained, and all conversations are encrypted both in transit and at rest.

What tools help monitor AI usage across SaaS platforms?

Several tools provide monitoring, auditing, and governance capabilities:

Microsoft Purview DSPM for AI captures prompts and responses for auditing, eDiscovery, and retention.
ChatGPT Enterprise Admin Console delivers usage insights for enterprise deployments.
Reco discovers shadow AI usage, detects sensitive data in prompts, and applies governance workflows across SaaS and AI environments.

Learn more about Reco’s Shadow AI Discovery to strengthen monitoring and governance.

How does Reco detect and prevent prompt injection attacks or data leaks?

Reco focuses on visibility and detection.
It discovers when sensitive data is shared with AI tools, classifies the content, and generates alerts for security teams.
While it does not directly block prompts, it provides governance workflows to help remediate risky behavior.
In combination, Microsoft Purview’s “Risky AI usage” policies can detect attempts such as prompt injection or access to protected materials.

Can Reco integrate with DLP or SIEM tools to extend AI security controls?

Yes. Here is how it works:

Reco integrates with SIEM and SOAR platforms to route alerts and support investigations, and it can complement DLP strategies by adding visibility into SaaS and AI interactions.
For content-level DLP and retention, Microsoft Purview provides native policies, while Reco adds discovery, classification, and governance.

See more about Reco at reco.ai.

Gal Nakash

ABOUT THE AUTHOR

Gal is the Cofounder & CPO of Reco. Gal is a former Lieutenant Colonel in the Israeli Prime Minister's Office. He is a tech enthusiast, with a background of Security Researcher and Hacker. Gal has led teams in multiple cybersecurity areas with an expertise in the human element.