It’s been a little over a year since generative artificial intelligence (GenAI) and large language models (LLMs) captured the world’s attention. Black hats immediately fell in love with them because the number of possible threat vectors increased exponentially. White hats fell in love with them because they held (and continue to hold) such tremendous promise for bringing advances to everyday life, such as speeding up the transactions and interactions that can make life better, like credit decisions and job offers, as well as for truly life-changing achievements, such as developing and testing new medicines, therapies, and procedures.
And although many organizations have taken advantage of the benefits of GenAI by deploying models across the enterprise to tremendous effect, such as increased productivity and streamlined operations, many more have not. The reason is they hold a genuine and not unfounded fear of things going terribly wrong, from cost and deployment miscalculations to employee misuse to data or system breaches. Gaps in the AI security apparatus, after all, can result in tremendous fallout from intellectual property or other sensitive data being shared via poorly written prompts or weak system safeguards, or malicious code making its way into the organization via unmonitored responses. However, there is an equal and opposite fear facing these hesitant decision-makers: getting left in the dust of mere automation as competitors ramp up their deployment of GenAI and AI-dependent systems, such as chatbots.
Both groups—the enthusiastic early adopters and the foot-draggers—face the same two critical business risks that have yet to be fully addressed by the model providers:
- Ensuring the accuracy of the information provided, including whether it is free from obvious and inherent biases (or hallucinations)
- Ensuring the validity of the information, including whether it is free from malicious content and meets the organization’s quality standards
If the information provided by LLMs does not meet the criteria implied by these issues, its value-add to the organization’s operations must be discounted somewhat, because it allows for exposure to negative outcomes. Not only have the LLM providers been slow to offer real solutions or assurances that these criteria can be met, a corporate shrug is their standard reaction when the tools (routinely) provide not only low-quality information, but utterly wrong responses, up to and including complete fiction when facts were requested.
CalypsoAI’s state-of-the-art LLM security platform solves for all of these issues by applying rigorous safeguards on the prompts going out and the responses returned. It conducts an instant, automated review of prompts by administrator-customized scanners that filter for source code, sensitive and personal data, toxicity, bias, legal content, and other user-established criteria that align with the organization’s acceptable use policy and other values, preventing the prompts from being sent without revision. It also scans incoming responses for malicious code and other user-identified content, and provides the option to require attributed human verification of the content. All interactions are recorded in detail, allowing full auditability and oversight.
This first-of-its-kind tool is a model agnostic, weightless, and adaptable trust layer that provides security and enablement in multi-model and multimodal environments. It integrates seamlessly with existing IT and cybersecurity infrastructures and its easy API connectivity, intuitive, user-friendly interface, and clear, simple documentation means there is no employee downtime required for training.
As the leader in the AI security and enablement domain, CalypsoAI knows how to provide the peace of mind decision-makers need to greenlight the safe, secure, ethical use of GenAI across the enterprise. But we would be very happy to convince you. Game on.
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