Common security risks for ChatGPT enterprise applications
Enterprise ChatGPT deployments face multiple security risks that differ from traditional application security because ChatGPT processes natural language inputs that can contain sensitive information. Its outputs are also non-deterministic, meaning the same prompt can produce different results. And with ChatGPT’s increasing popularity, shadow AI is becoming more and more prevalent, exposing enterprises to a growing security gap.
There are five primary risks:
Data theft occurs when sensitive enterprise data is intercepted during API calls or extracted from improperly secured storage. Attackers can exploit weak API endpoints or misconfigured access controls to capture confidential business information as it flows between your systems and OpenAI's infrastructure.
Data leaks happen when ChatGPT inadvertently exposes PII or intellectual property through its responses. This risk increases significantly during fine-tuning, when training data that hasn't been properly sanitized can become embedded in the model's behavior and potentially surface in outputs to other users.
Malicious code generation occurs when attackers craft prompts that bypass ChatGPT's safety filters. While the model won't intentionally produce harmful code, adversarial prompts can manipulate it into generating code that enables unauthorized system access or automates attack sequences.
Output misuse is when attackers leverage ChatGPT-generated content for deception. In enterprise contexts, this includes generating convincing phishing emails, fabricating legal or financial advice, or creating fraudulent communications that appear to come from legitimate business sources.
Unauthorized access and impersonation exploits ChatGPT's ability to mimic communication styles. Attackers can craft messages that impersonate executives, IT support, or trusted vendors, making social engineering attacks more convincing and harder to detect.
Robust security protocols are crucial for protecting against such threats, whether unintentional or the result of cyber attacks, and for safeguarding enterprise applications.
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Best practices for securing ChatGPT deployments
Securing ChatGPT deployments requires controls that address AI-specific risks while building on your existing security foundation. The practices below assume you already have monitoring, logging, and endpoint protection in place. These five practices focus specifically on the unique challenges posed by ChatGPT.
1. Maintain API versioning
OpenAI regularly patches vulnerabilities and updates safety filters, so running outdated versions leaves your deployment exposed to known risks. Keep everything up to date with a few regular practices:
Evaluate ChatGPT Enterprise—plans include security controls like SSO, audit logging, and data isolation that aren't available in standard tiers.
Automate update monitoring by integrating OpenAI release tracking into your DevSecOps workflows to catch security-relevant changes. Be sure you periodically migrate your APIs to the latest stable versions of ChatGPT.
Track AI-specific vulnerabilities by subscribing to OpenAI security bulletins and monitoring CVE databases for LLM-related threats.
Minimize your attack surface and limit the number of models, plugins, and integrations to reduce potential entry points.
2. Implement zero-trust security access
ChatGPT access often bypasses traditional network perimeters since employees use it from personal devices, home networks, and mobile apps, mirroring the enterprise network trends like BYOD that zero-trust architecture was designed to address. Zero-trust security ensures every request is verified regardless of where it originates:
Require MFA for all ChatGPT access for both web interfaces and API integrations.
Secure API endpoints with strong authentication, rate limiting, and anomaly detection for any systems calling OpenAI APIs.
Enforce TLS encryption, ensuring all communication between your systems and OpenAI use encrypted channels.
Deploy behavioral analytics to monitor for unusual patterns like bulk data extraction, off-hours access, or requests from unexpected locations.
3. Limit PII and IP data
Minimizing sensitive data that's directly processed by ChatGPT reduces the risk of leaks and unauthorized access:
Encrypt data in transit and at rest.
Obtain user consent for processing any personal data.
Anonymize and de-identify sensitive data before feeding it into ChatGPT, protecting individuals' privacy while still utilizing the tool's full capabilities.
Establish strict data-retention policies to limit how long sensitive information is stored and processed.
Audit data flows on a regular basis.
4. Employ content moderation
Safeguard your ChatGPT outputs from being misused or misaligned with your business goals:
Check for copyright infringement or unauthorized use of proprietary data in ChatGPT-generated content, particularly in client-facing materials.
Implement output-filtering mechanisms to flag or block inappropriate, offensive, or biased responses before they reach end users or stakeholders.
Reduce output homogenization by customizing responses or using prompts that encourage unique and varied results, avoiding standardized or repetitive answers.
Always verify the accuracy and source of critical information produced by ChatGPT.
5. Education and Governance
Proactive, people-centric security is essential for minimizing human error and avoiding vulnerabilities altogether:
Develop a codified AI policy that outlines acceptable use, security protocols, and clear responsibilities for how ChatGPT is deployed and managed across the organization.
Regularly train employees on the risks associated with AI and ChatGPT, especially focusing on responsible data-sharing practices and awareness of common social engineering attacks.
Perform regular risk assessments to identify and address vulnerabilities, and ensure alignment with the latest security and compliance standards.
Establish an incident response plan for AI and GenAI security incidents.
By following these best practices, organizations can secure ChatGPT deployments while harnessing the GenAI model's potential for enterprise-scale innovation. For more official security guidelines, refer to OpenAI's safety best practices.
How to ensure regulatory compliance with ChatGPT
ChatGPT creates a compliance gap that many organizations overlook—research shows only 18% of enterprises have a dedicated council for responsible AI governance. When employees paste customer data, patient information, or financial records into prompts, that data may be processed outside your controlled environment and potentially used for model training.
However, General Data Protection Regulation (GDPR) requires lawful, transparent data processing with documented consent, and the Health Insurance Portability and Accountability Act (HIPAA) mandates strict safeguards for protected health information. Both frameworks apply to data shared with ChatGPT, not just data stored in your own systems.
OpenAI's SOC 2 Type 2 and CSA STAR Level 1 certifications validate GDPR and HIPAA’s infrastructure security, but compliance responsibility for how your organization uses the tool remains with you.
To meet these regulatory requirements, enterprises should at a minimum:
Familiarize themselves with relevant laws and guidelines.
Perform regular security audits to detect vulnerabilities and address them swiftly to maintain robust defenses.
Maintain transparency in the decision-making processes of AI models, especially when based on sensitive information.
Regular assessments and audits can help prevent violations and ensure AI deployments meet compliance standards. For specific compliance guidelines and resources on securing ChatGPT, visit OpenAI's Trust and Safety resources.
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How Wiz AI Security helps you secure ChatGPT in production
Most security tools can't see what's happening inside your AI pipelines. They lack visibility into which employees are using ChatGPT, what data flows through prompts, and whether your configurations expose sensitive information. According to Wiz's AI Security Readiness report, 25% of organizations don't know what AI services are running in their environment, underscoring the visibility challenge.
Wiz AI Security can protect your stack with careful scanning, monitoring, integration, and testing, helping your team stay ahead of attacks while continuously strengthening overall security:
Connect directly to OpenAI through a SaaS connector, giving you the same visibility into ChatGPT deployments you have for other cloud workloads.
Map relationships across tools, models, agents, data, and infrastructure with Wiz Cloud.
Scan for security gaps, like unsafe patterns, vulnerable dependencies, and exposed credentials, with Wiz Code.
Monitor behavior to identify rogue agents, anomalous data egress, and prompt injection attacks with Wiz Defend.
Implement AI agents across processes: Red Agent to simulate attacks and find vulnerabilities, Blue Agent to hunt threats and investigate alerts, and Green Agent to mark priority fixes with AI-assisted remediation.
Securing ChatGPT without slowing down innovation
ChatGPT adoption doesn't have to come with security blind spots. With the right visibility into AI data flows, user activity, and configuration risks, you can enable your teams to use generative AI tools while maintaining strong governance and compliance standards.
The key is treating ChatGPT like any other cloud workload: inventory it, monitor it, and apply consistent security policies across your environment. Wiz AI-SPM gives security teams that unified view, connecting ChatGPT deployments to the broader cloud context so you can identify risks before they become incidents.
Ready to see how your ChatGPT deployments look from a security perspective? Book a demo to explore how Wiz maps AI risk across your cloud environment.
