ChatGPT security refers to the practices and controls that protect users and businesses from risks that arise during interactions with ChatGPT. ChatGPT processes user inputs to generate responses and may log certain activity for reliability and abuse detection. Enterprise offerings such as ChatGPT Business and ChatGPT Enterprise include security features like encryption, admin controls, and a guarantee that prompts and company data are not used to train models, which reduces but does not eliminate risk.
Despite those built-in security functions, ChatGPT security concerns emerge when employees share internal documents, customer data, code, or regulated information inside prompts, or when ChatGPT is connected to other systems without proper oversight. These interactions can lead to data leakage, compliance gaps, or inaccurate outputs that influence business decisions.
ChatGPT security also extends beyond individual prompts to the growing use of custom GPTs and agentic tools that employees can create without formal approval. These autonomous components can connect to internal systems, access sensitive data, and perform actions without centralized visibility or governance, introducing an expanded and often hidden risk.
A common misconception is that ChatGPT is “secure by default.” In reality, some of the biggest risks come from how people use the tool, not from model-level flaws. Another misconception is that all ChatGPT data is used for training. OpenAI clarifies that this depends on the account type and settings. Ultimately, ChatGPT security entails managing safe prompting, controlled integrations, and responsible data use across teams.
When teams talk about “ChatGPT security issues,” they’re usually referring to how end users interact with the system, such as: what they type in, what they copy out, and which systems they connect ChatGPT to. Even with OpenAI’s security features, such as encryption, compliance controls, and usage policies, unsafe interactions and integrations can still pose significant risk. The following table outlines the key security issues associated with ChatGPT:
For enterprises, ChatGPT security issues extend beyond individual user behavior. Once ChatGPT becomes part of everyday workflows, including content creation, data analysis, customer responses, coding assistance, or internal decision-making, its risks intersect with organizational policies, regulatory obligations, and access controls. The following subsections outline the business-specific challenges that arise when ChatGPT is deployed across business units.
Even when employees understand general safe-prompting guidelines, businesses face ongoing exposure risks at scale. Teams may unintentionally include strategic plans, product roadmaps, customer information, contract excerpts, or internal troubleshooting notes in prompts. While this type of oversharing has already been introduced in earlier sections, its business impact merits closer attention.
Organizations risk the loss of intellectual property, leakage of sensitive operational information, and inadvertent disclosure of regulated data. These exposures, in turn, complicate legal holds, incident response, and contractual confidentiality obligations with clients and partners.
When ChatGPT chats or custom GPTs use web-browsing capabilities, parts of a user’s prompt may be incorporated into the external search request, sending sensitive information beyond the enterprise tenant. While this data is not made public, it can be processed and logged by external search and browsing services, creating an additional exposure path that does not exist when all interactions remain contained within the organization’s environment.
Enterprises often operate under multiple regulatory regimes. When employees use ChatGPT without the appropriate safeguards, organizations must consider whether prompts, generated outputs, or data passed through integrations contain information governed by GDPR, HIPAA, US state privacy statutes, PCI DSS, or other industry-specific mandates.
The challenge is not only preventing improper data sharing but also ensuring auditability, retention alignment, and policy enforcement across distributed teams. Businesses must verify that internal ChatGPT usage aligns with their existing compliance frameworks and that they can document how AI-assisted workflows handle sensitive data.
Businesses increasingly integrate ChatGPT into internal applications, Slack channels, ticketing systems, and custom workflows. These integrations typically involve custom GPTs and agents, which often connect to internal knowledge bases, APIs, or enterprise systems.
These integrations introduce additional attack surfaces and vulnerabilities, such as misconfigured API keys, excessive permissions, insecure data flows, or actions that pull or push information across systems.
If not properly governed, these extended AI components can expose sensitive data to unintended environments or allow malicious prompts to influence downstream systems. As ChatGPT becomes embedded into enterprise architecture, securing these integrations becomes just as important as securing individual user prompts.
The risks outlined above aren’t theoretical. Organizations have already experienced real incidents where unsafe prompting, flawed integrations, or unmonitored AI usage led to exposure. These examples demonstrate how quickly ChatGPT-related risks can materialize in practical business settings.
Tenable’s 2025 “HackedGPT” research disclosed multiple vulnerabilities enabling novel prompt-injection attacks against ChatGPT. These flaws allowed attackers to craft inputs that manipulated system behavior, bypassed guardrails, or extracted private data.
Some attacks worked by embedding hidden instructions inside user-generated content or external data sources that ChatGPT processed, causing the model to reveal sensitive information or execute unauthorized actions.
Because the prompts looked benign to users, the injected instructions operated silently, making them difficult to detect. The findings show how prompt injection can exploit integrations, plugins, or context ingestion, turning seemingly harmless text into a vector for data leakage and misuse.
A recurring pattern in real-world incidents involves employees unintentionally exposing confidential information through AI prompts. Samsung’s 2023 leak remains the most well-known example: engineers pasted proprietary source code and internal notes into ChatGPT, prompting the company to restrict generative-AI use across business units.
However, similar events continue today. A 2025 LayerX report found that 77% of employees using AI tools shared sensitive company data, often from unmanaged or personal accounts, creating untracked exposure paths and compliance gaps. These cases show that data leakage frequently stems not from system compromise but from everyday workflows that lack AI-specific governance.
A 2025 Reuters investigation demonstrated how generative AI can significantly increase the effectiveness of social-engineering attacks. In a controlled experiment, researchers used tools including ChatGPT, Grok, Meta AI, and DeepSeek to generate phishing-style emails impersonating major U.S. banks and the IRS.
These messages were then sent to volunteer test subjects (not real customers) to evaluate how convincing AI-crafted scams could be. The results showed that recipients were more likely to click on links or respond to the AI-generated messages than to traditional phishing emails. Although no real victims were targeted, the study highlights how generative AI lowers the skill barrier and can amplify the sophistication and scalability of phishing campaigns.
OpenAI implements multiple security, privacy, and safety controls to reduce the risks associated with ChatGPT use. While these controls do not eliminate the organizational challenges described earlier, they provide guardrails that help minimize accidental data exposure, model misuse, and unauthorized access.
To reduce the risks outlined earlier, enterprise teams need consistent controls that guide how employees interact with ChatGPT across departments. The following best practices focus on preventing avoidable exposure, strengthening day-to-day usage patterns, and building governance structures that scale with AI adoption.
Even with strong security practices, certain technical characteristics of large language models introduce risks that organizations must anticipate. These limitations do not indicate flaws in ChatGPT itself, they are inherent to how generative AI systems operate.
Understanding these constraints helps teams design safer workflows and avoid relying on the model in ways that create downstream exposure.
ChatGPT can generate outputs that are inaccurate, incomplete, or entirely fabricated, often presented with an air of confidence. In regulated, financial, or operational settings, these hallucinations can influence decision-making, produce misleading summaries, or introduce errors into customer communications or reports.
When employees assume 100% correctness or fail to apply verification steps, the resulting mistakes may create compliance issues, propagate misinformation, or affect business judgment.
Because ChatGPT responses vary across sessions, users, and prompt phrasing, teams cannot always reproduce prior outputs exactly. This non-deterministic behavior complicates auditability when organizations must demonstrate how a decision was generated or show consistent reasoning across cases.
In environments with strict controls (e.g., legal or healthcare), this variability can create documentation gaps or challenge the ability to trace how an AI-assisted workflow influenced an outcome.
ChatGPT generates responses without exposing its internal reasoning or decision pathways. As a result, teams cannot always determine why the model produced a specific answer, whether external information influenced its output, or how it interpreted the prompt.
This opacity contributes to challenges in risk assessment and makes it harder to detect when prompts were manipulated, when instructions were implicitly overridden, or when the model integrated context in unintended ways. The lack of explainability increases the need for human oversight, validation, and controlled usage patterns.
Custom GPTs and agentic workflows can retrieve data or execute actions autonomously, yet their underlying system prompts, permissions, and connections are rarely documented or centrally tracked.
As a result, security teams can’t reliably audit what these agents accessed, why they made certain decisions, or how their behavior evolved over time. This lack of visibility creates shadow-IT-like blind spots, but with automation capabilities that can amplify data exposure, misconfiguration, or misuse.
While OpenAI provides foundational safeguards, organizations still need visibility, governance, and controls that operate across their own data, workforce, and AI workflows. Opsin adds this missing layer by continuously monitoring AI usage, enforcing policy, and preventing data exposure before it occurs.
The following capabilities address the enterprise risks discussed throughout this article and help teams deploy ChatGPT safely.
ChatGPT offers powerful capabilities for accelerating work across the enterprise, but its benefits come with meaningful security, privacy, and compliance risks. As this article shows, many of the most significant issues arise not from the model itself, but from how employees interact with it, how AI tools integrate into existing systems, and how organizations govern sensitive data.
Addressing these challenges requires a combination of safe-use practices, clear policies, and technical controls that extend beyond the protections built into ChatGPT. With the right safeguards in place, enterprises can unlock the value of generative AI while maintaining the level of security, oversight, and accountability their environments demand.
No, Enterprise reduces risk but cannot control what employees type or upload.
Learn more about AI Security Blind Spots.
The most effective control is isolating untrusted input and applying multi-layer validation before execution.
Opsin’s Magic Trick of Prompt Injection analysis provides additional threat-model detail.
You need structured capture of inputs, outputs, policy decisions, and context sources.
Opsin intercepts high-risk prompts in real time and blocks them at the boundary.
See a real customer example of oversharing reduction in action.
Opsin provides full, exportable audit trails covering prompts, detected risks, and enforced policies.
For industries with strict regulatory requirements, learn more about our healthcare & life sciences solution.
ChatGPT security refers to the practices and controls that protect users and businesses from risks that arise during interactions with ChatGPT. ChatGPT processes user inputs to generate responses and may log certain activity for reliability and abuse detection. Enterprise offerings such as ChatGPT Business and ChatGPT Enterprise include security features like encryption, admin controls, and a guarantee that prompts and company data are not used to train models, which reduces but does not eliminate risk.
Despite those built-in security functions, ChatGPT security concerns emerge when employees share internal documents, customer data, code, or regulated information inside prompts, or when ChatGPT is connected to other systems without proper oversight. These interactions can lead to data leakage, compliance gaps, or inaccurate outputs that influence business decisions.
ChatGPT security also extends beyond individual prompts to the growing use of custom GPTs and agentic tools that employees can create without formal approval. These autonomous components can connect to internal systems, access sensitive data, and perform actions without centralized visibility or governance, introducing an expanded and often hidden risk.
A common misconception is that ChatGPT is “secure by default.” In reality, some of the biggest risks come from how people use the tool, not from model-level flaws. Another misconception is that all ChatGPT data is used for training. OpenAI clarifies that this depends on the account type and settings. Ultimately, ChatGPT security entails managing safe prompting, controlled integrations, and responsible data use across teams.
When teams talk about “ChatGPT security issues,” they’re usually referring to how end users interact with the system, such as: what they type in, what they copy out, and which systems they connect ChatGPT to. Even with OpenAI’s security features, such as encryption, compliance controls, and usage policies, unsafe interactions and integrations can still pose significant risk. The following table outlines the key security issues associated with ChatGPT:
For enterprises, ChatGPT security issues extend beyond individual user behavior. Once ChatGPT becomes part of everyday workflows, including content creation, data analysis, customer responses, coding assistance, or internal decision-making, its risks intersect with organizational policies, regulatory obligations, and access controls. The following subsections outline the business-specific challenges that arise when ChatGPT is deployed across business units.
Even when employees understand general safe-prompting guidelines, businesses face ongoing exposure risks at scale. Teams may unintentionally include strategic plans, product roadmaps, customer information, contract excerpts, or internal troubleshooting notes in prompts. While this type of oversharing has already been introduced in earlier sections, its business impact merits closer attention.
Organizations risk the loss of intellectual property, leakage of sensitive operational information, and inadvertent disclosure of regulated data. These exposures, in turn, complicate legal holds, incident response, and contractual confidentiality obligations with clients and partners.
When ChatGPT chats or custom GPTs use web-browsing capabilities, parts of a user’s prompt may be incorporated into the external search request, sending sensitive information beyond the enterprise tenant. While this data is not made public, it can be processed and logged by external search and browsing services, creating an additional exposure path that does not exist when all interactions remain contained within the organization’s environment.
Enterprises often operate under multiple regulatory regimes. When employees use ChatGPT without the appropriate safeguards, organizations must consider whether prompts, generated outputs, or data passed through integrations contain information governed by GDPR, HIPAA, US state privacy statutes, PCI DSS, or other industry-specific mandates.
The challenge is not only preventing improper data sharing but also ensuring auditability, retention alignment, and policy enforcement across distributed teams. Businesses must verify that internal ChatGPT usage aligns with their existing compliance frameworks and that they can document how AI-assisted workflows handle sensitive data.
Businesses increasingly integrate ChatGPT into internal applications, Slack channels, ticketing systems, and custom workflows. These integrations typically involve custom GPTs and agents, which often connect to internal knowledge bases, APIs, or enterprise systems.
These integrations introduce additional attack surfaces and vulnerabilities, such as misconfigured API keys, excessive permissions, insecure data flows, or actions that pull or push information across systems.
If not properly governed, these extended AI components can expose sensitive data to unintended environments or allow malicious prompts to influence downstream systems. As ChatGPT becomes embedded into enterprise architecture, securing these integrations becomes just as important as securing individual user prompts.
The risks outlined above aren’t theoretical. Organizations have already experienced real incidents where unsafe prompting, flawed integrations, or unmonitored AI usage led to exposure. These examples demonstrate how quickly ChatGPT-related risks can materialize in practical business settings.
Tenable’s 2025 “HackedGPT” research disclosed multiple vulnerabilities enabling novel prompt-injection attacks against ChatGPT. These flaws allowed attackers to craft inputs that manipulated system behavior, bypassed guardrails, or extracted private data.
Some attacks worked by embedding hidden instructions inside user-generated content or external data sources that ChatGPT processed, causing the model to reveal sensitive information or execute unauthorized actions.
Because the prompts looked benign to users, the injected instructions operated silently, making them difficult to detect. The findings show how prompt injection can exploit integrations, plugins, or context ingestion, turning seemingly harmless text into a vector for data leakage and misuse.
A recurring pattern in real-world incidents involves employees unintentionally exposing confidential information through AI prompts. Samsung’s 2023 leak remains the most well-known example: engineers pasted proprietary source code and internal notes into ChatGPT, prompting the company to restrict generative-AI use across business units.
However, similar events continue today. A 2025 LayerX report found that 77% of employees using AI tools shared sensitive company data, often from unmanaged or personal accounts, creating untracked exposure paths and compliance gaps. These cases show that data leakage frequently stems not from system compromise but from everyday workflows that lack AI-specific governance.
A 2025 Reuters investigation demonstrated how generative AI can significantly increase the effectiveness of social-engineering attacks. In a controlled experiment, researchers used tools including ChatGPT, Grok, Meta AI, and DeepSeek to generate phishing-style emails impersonating major U.S. banks and the IRS.
These messages were then sent to volunteer test subjects (not real customers) to evaluate how convincing AI-crafted scams could be. The results showed that recipients were more likely to click on links or respond to the AI-generated messages than to traditional phishing emails. Although no real victims were targeted, the study highlights how generative AI lowers the skill barrier and can amplify the sophistication and scalability of phishing campaigns.
OpenAI implements multiple security, privacy, and safety controls to reduce the risks associated with ChatGPT use. While these controls do not eliminate the organizational challenges described earlier, they provide guardrails that help minimize accidental data exposure, model misuse, and unauthorized access.
To reduce the risks outlined earlier, enterprise teams need consistent controls that guide how employees interact with ChatGPT across departments. The following best practices focus on preventing avoidable exposure, strengthening day-to-day usage patterns, and building governance structures that scale with AI adoption.
Even with strong security practices, certain technical characteristics of large language models introduce risks that organizations must anticipate. These limitations do not indicate flaws in ChatGPT itself, they are inherent to how generative AI systems operate.
Understanding these constraints helps teams design safer workflows and avoid relying on the model in ways that create downstream exposure.
ChatGPT can generate outputs that are inaccurate, incomplete, or entirely fabricated, often presented with an air of confidence. In regulated, financial, or operational settings, these hallucinations can influence decision-making, produce misleading summaries, or introduce errors into customer communications or reports.
When employees assume 100% correctness or fail to apply verification steps, the resulting mistakes may create compliance issues, propagate misinformation, or affect business judgment.
Because ChatGPT responses vary across sessions, users, and prompt phrasing, teams cannot always reproduce prior outputs exactly. This non-deterministic behavior complicates auditability when organizations must demonstrate how a decision was generated or show consistent reasoning across cases.
In environments with strict controls (e.g., legal or healthcare), this variability can create documentation gaps or challenge the ability to trace how an AI-assisted workflow influenced an outcome.
ChatGPT generates responses without exposing its internal reasoning or decision pathways. As a result, teams cannot always determine why the model produced a specific answer, whether external information influenced its output, or how it interpreted the prompt.
This opacity contributes to challenges in risk assessment and makes it harder to detect when prompts were manipulated, when instructions were implicitly overridden, or when the model integrated context in unintended ways. The lack of explainability increases the need for human oversight, validation, and controlled usage patterns.
Custom GPTs and agentic workflows can retrieve data or execute actions autonomously, yet their underlying system prompts, permissions, and connections are rarely documented or centrally tracked.
As a result, security teams can’t reliably audit what these agents accessed, why they made certain decisions, or how their behavior evolved over time. This lack of visibility creates shadow-IT-like blind spots, but with automation capabilities that can amplify data exposure, misconfiguration, or misuse.
While OpenAI provides foundational safeguards, organizations still need visibility, governance, and controls that operate across their own data, workforce, and AI workflows. Opsin adds this missing layer by continuously monitoring AI usage, enforcing policy, and preventing data exposure before it occurs.
The following capabilities address the enterprise risks discussed throughout this article and help teams deploy ChatGPT safely.
ChatGPT offers powerful capabilities for accelerating work across the enterprise, but its benefits come with meaningful security, privacy, and compliance risks. As this article shows, many of the most significant issues arise not from the model itself, but from how employees interact with it, how AI tools integrate into existing systems, and how organizations govern sensitive data.
Addressing these challenges requires a combination of safe-use practices, clear policies, and technical controls that extend beyond the protections built into ChatGPT. With the right safeguards in place, enterprises can unlock the value of generative AI while maintaining the level of security, oversight, and accountability their environments demand.
