GENERATIVE AI

Generative AI encompasses various models, including text-to-text, image-to-image, and multi-modal models. These complex models come with unique risks. Key concerns include:

• Hallucination and False Information: AI generating misleading or incorrect content.
• Bias and Fairness: Producing biased outputs that may discriminate against protected classes.
• Privacy Infringement: Leaking private or sensitive information.
• Intellectual Property Violations: Creating content based on IP-protected material without permission or producing derivative works.
• Harmful Content: Generating offensive, malicious, or illegal content.
• Environmental Impact: Training large models increases energy consumption, posing environmental concerns.

• Consistent Monitoring for Undetected AI Models: Regularly scheduled scans to detect AI usage in models and EUCs can uncover risks before they result in errors, ensuring regulatory compliance.
• Comprehensive AI Testing Suite: Implementing a robust testing suite to detect and control AI includes tests for data drift, validity, reliability, fairness, interpretability, and code quality. Consistent documentation of results ensures transparency and accountability.
• LLM Vulnerability Testing: Testing for biases, fairness, harmful content, and privacy issues helps stress-test models before customer deployment.
• Explainable LLMs: Using content attribution to trace the origin of data used in AI responses helps mitigate errors and prevent the spread of incorrect information.
• LLM Hallucination Testing: Monitoring the rates of hallucination, or incorrect responses, in LLMs ensures accuracy and reliability. Leveraging the latest developments in RAG models and Challenger LLMs is crucial.
• Implementing Controls and Accountability Measures: Managing access to EUC models and tools with audit trails and approval workflows can mitigate risks associated with Shadow AI.

CIMCON Software has been a leader in managing AI, EUC, and Model Risk for over 25 years, trusted by over 800 customers worldwide. We are ISO 27001 Certified for Information Security and have offices in the USA, London, and Asia Pacific. Our risk management platform supports best practices and policy automation, including an EUC & Model Inventory, Risk Assessment, Cybersecurity & Privacy Vulnerability Identification, and an EUC Map showing relationships between EUCs and models. Our AIValidator tool automates model testing and documentation, available as a no-code tool or a Python package.

According to McKinsey, generative AI could surpass the added value generated by financial services from other AI technologies, potentially totaling up to $340 billion. Financial institutions are developing use cases in sales, marketing, customer operations, and software development, projecting up to a tenfold increase in productivity. Managing the challenges of GenAI involves proactive monitoring to unleash its benefits while minimizing risks. This includes monitoring hidden AI models, third-party applications, and submissions to third-party AI generators.