Generative AI Implementation Risks
Risk Category | Description | Potential Impact | Mitigation with Continuum |
---|---|---|---|
Data Privacy and Confidentiality | Inadvertent sharing of confidential or private information with GenAI systems | Legal liabilities, regulatory penalties, reputational damage | Continuum's secure model hosting and data handling practices ensure strict control over sensitive information |
Legal and Regulatory Compliance | Questions around ownership of generated content and potential liabilities | Legal disputes, financial penalties, reputational harm | Continuum stays up-to-date on evolving regulations and provides guidance on compliant use of GenAI |
Insecure Code Generation | Reliance on untested AI-generated code introducing vulnerabilities | Data breaches, system compromises, operational disruptions | Continuum's rigorous testing and validation processes ensure the security and reliability of generated code |
Trust and Reputation | Inaccurate or biased GenAI outputs published under company name | Loss of customer trust, damage to brand reputation, financial losses | Continuum's custom model training and output monitoring mitigate the risk of inaccurate or biased results |
Workflow Disruption | GenAI changing workflows and being used by employees in various roles | Inconsistent practices, decreased productivity, security gaps | Continuum works closely with clients to integrate GenAI into workflows while maintaining security and efficiency |
Prompt Injection Attacks | Malicious prompts manipulating GenAI systems to produce harmful outputs | Data leakage, system compromise, reputational damage | Continuum implements robust prompt filtering and validation to prevent prompt injection attacks |
Voice Spoofing Attacks | Synthetic voice generation used for impersonation and fraud | Financial losses, reputational harm, erosion of trust | Continuum develops advanced detection capabilities to identify and prevent voice spoofing attacks |
Model Bias and Fairness | GenAI models reflecting societal biases or discriminating against certain groups | Legal liabilities, reputational damage, erosion of public trust | Continuum employs rigorous testing and auditing to identify and mitigate model biases |
Lack of Interpretability | Difficulty understanding and explaining GenAI decision-making processes | Regulatory non-compliance, lack of accountability, erosion of trust | Continuum prioritizes interpretability and provides clear explanations of model outputs |
Insider Threats | Malicious insiders exploiting GenAI access for unauthorized purposes | Data theft, system sabotage, reputational harm | Continuum implements strict access controls and monitoring to detect and prevent insider threats |
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