Enterprise Risks in Chat GPT
Generative AI (GenAI) has introduced new enterprise risks that must be properly evaluated in a business context. These risks include technological or procedural expansions of current hazards, legal and regulatory risks, and wholly new dangers. A reference table is provided as guidelines for organizations to assess these risks.
Enterprises can reduce the negative impact of GenAI usage by opting out of having user prompt data used to train future models, accepting a data retention policy of 30 days, declaring a Risk Exception, or investigating an on-premises option.
The authors seek to address data exposure issues related to GenAI and ChatGPT by using large language models (LLMs) to make user input and private data available to rivals. GenAI technologies may use user input for model development, but OpenAI’s stance on this is against it due to potential dangers and issues with biased models.
A. Data Privacy and Confidentiality
Private information and non-public enterprise The application of GenAI in the enterprise may result in the access and processing of sensitive information, intellectual property, source code, trade secrets, and other proprietary information. data, either direct user input or the API, including customer or private information and confidential information. This has previously been noted as a problem here.
- Sending secret and private information outside of an organization’s network can lead to legal and compliance issues, as well as risks of information exposure, under CCPA, GDPR, and HIPAA.
- GenAI technologies such as ChatGPT have been handed over to OpenAI, but the third-party SaaS is not yet integrated. This means that users will not be able to view data in real time, and future models may not use user input.
- Corporate documentation states that OpenAI’s API is not maintained for more than 30 days and is opt-out by default, while ChatGPT is opt-in by default and charged a fee. However, provided information is vulnerable to storage and processing risks.
B. Enterprise, SaaS, and Third-party Security
Non-public enterprise data, third- and fourth-party software
CISOs are concerned that data exchange with third parties will increase due to the widespread use of GenAI and integrations in third-party apps, leading to less predictable patterns.
- Risks in the supply chain can be divided into three categories: relying on third-party security, relying on GenAI technologies, and relying on third-party quality assurance.
- GenAI platforms may expose sensitive data such as customer data, financial information, and proprietary business information if their systems and infrastructure are not secure.
- GenAI and ChatGPT are being integrated into third-party platforms such as Microsoft Azure OpenAPI and Office 365, creating a potential risk.
- GenAI platforms are a high-value target for threat actors due to their limited number and increasing use.
C. AI Behavioral Vulnerabilities
Model operator, non-public enterprise data
Actors may use or cause models to be used in ways that reveal sensitive information about the model or cause the model to be damaged. do acts that are contrary to the design’s aims.
- Attackers can use GenAI systems to circumvent AI behavior and make it execute unexpected tasks, which can have a negative influence on organizations and stakeholders.
- Third-party applications with GenAI APIs can allow attackers to access user data, potentially allowing them to take actions on behalf of the user.
- Injection attacks can be used to gain unauthorized access to business systems.
D. Legal and Regulatory
- Regulatory Consideration: GenAI must be used following data privacy rules, such as GDPR, PIPEDA, and CCPA. Italy’s data protection regulator has temporarily prohibited the use of ChatGPT, and Germany is reviewing the issue.
- Legal Consideration: GenAI used in consumer-facing communications can be regulated, resulting in legal or regulatory consequences. ChatGPT and chatbot services must be disclosed to clients to avoid potential legal action.
Threat Actor Evolution
A. Enterprise readiness, third parties
Threat actors use GenAI for malevolent objectives, such as phishing attacks and social engineering. To address this, security awareness training and other social engineering measures must be re-evaluated, and controls must be mitigated.
B. Organization’s legal exposure
GenAI models are trained on a wide range of data, including an unknown amount of copyrighted and private content, causing ownership and licensing concerns between the organization and third parties.
- GenAI models have been accused of utilizing material generated by others, which could lead to intellectual property violations and plagiarism, as the same material can be provided to multiple parties.
- GenAI models may inadvertently infringe on copyrighted material without authorization from dataset owners.
- The US Copyright Office has recommended denying copyright protection for GenAI works, allowing them to be freely used and copied.
- GenAI may return code with proprietary content, such as GPL 3, which could be legally binding for organizations to distribute. It is important to consult with an attorney if any infractions occur.
- Policies should be based on current intellectual property concepts.
C. Insecure Code Generation
- Software development projects and developers GenAI-generated code can be used without sufficient security audit or review, leading to the deployment of insecure programs in different systems and as “ground truth” for future model learning.
- Organization’s brand and reputation GenAI’s output can be delivered with significant reputational risks, including erroneous, damaging, biased, or humiliating content, as well as safety concerns such as doxxing or hate speech.
- The current generation of GenAI models has been seen to provide erroneous, inaccurate, incorrect, and deceptive data.
- Using AI outputs without validation can lead to inaccurate assertions and facts, which can result in legal consequences such as libel.
A. Software Security Vulnerabilities
Non-public enterprise data and system integrity
- GenAI apps must be updated and secured with proper controls against traditional software vulnerabilities and their interaction with developing AI vulnerabilities.
- GenAI systems are vulnerable to software vulnerabilities and AI flaws, increasing the risk.
- Attackers can exploit flaws in front-end models to manipulate back-end models, triggering SQL injections when appended to model output.
B. Availability, Performance, and Costs
- Enterprise systems’ Resilience
GenAI and OpenAI can present operational risks such as system downtime, performance, and failure. User mistakes must be included in threat modeling and architectural planning, and backup and disaster recovery methods are required. Operating an LLM can be costly, with each response costing “single digit cents” according to OpenAI.
- Regulatory Compliance
AI is gaining traction due to ethical standards for safety, security, fairness, transparency, explain ability, and general responsibility. Legal and regulatory structures are already incorporating these principles, so it is important to screen for potential impacts and consider mitigation measures. CISOs can also influence rules and educate authorities. ESG issues should also be evaluated.
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