100% Secure, Private and Confidential way to use Generative AI for Contracts
Everyone has heard about the potential of Large Language Models (LLMs) like GPT3.5/4, Bard, Claude, etc. Want to use them to make your contract drafting, reviews and management more efficient and rigorous, while keeping your data safe? However, there have been issues like confidential data getting leaked via ChatGPT and lack of clarity on ToS & policies around data usage from LLM providers.
ContractKen is the safest way to leverage AI for Contracts at your enterprise
Our APIs, chatUI, and proprietary algorithms, automatically mask, anonymize or warn users about sensitive data, based on policies set by IT. Users get the most powerful AI apps like GPT-4 (the private and most powerful version of ChatGPT-4), and others, whilst the Enterprise can control usage with confidence that data is secured and Audit Logged.
Audit Logs
ContractKen gives you granular audit logs on all data shared with AI providers. Understand who is sharing what data, with whom, with full transparency into the MSAs and Terms of Service governing that data.
Automatic Anonymization
ContractKen automatically redacts sensitive data (client names, dates, individual names, etc.), phrases, clause language, commercial information, or entire categories of data (SSNs, PHI etc.) before the data leaves your organization. Full control for Infosec teams. Frictionless for users.
WYSIWYG
ContractKen shows you the actual anonymized version of the document at the click of a button so that the user is comfortable about privacy. All of the anonymized versions are saved separately on our server to ensure a full audit trail - so that you are not only using LLMs safely but can also prove it, if needed later.
You define what is sensitive, private & confidential
Your Infosec admin teams have the ability to define what words, text, phrases fall into sensitive, private and confidential categories. Our algorithms automatically anonymize PII elements, financial information, PHIs, etc. You can add what constitutes proprietary information, custom clause language or even style and tone of language to be preserved.
Our Future Roadmap on Data Privacy and Confidentiality
ContractKen is committed to making use of Generative AI 100% safe, private and secure for Contracts, from inside Microsoft Word. While we have implemented the industry leading ‘Moderation Layer’ solution, we are not sitting on our laurels. Our engineering team is working on testing the use of GANs (Generative Adversarial Networks) for preserving privacy.
Generative Adversarial Networks (GANs) can be used to generate synthetic data that preserves the statistical characteristics of the original data without compromising privacy. This approach can be particularly useful in preserving privacy and confidentiality when using Large Language Models (LLMs) like GPT-3.5 for contract review and analysis.
Here's a high-level overview of how this can work:
1. Training GANs: First, train a GAN on a dataset of contract texts. The GAN consists of two parts: a generator and a discriminator. The generator's role is to create new synthetic contract text data, and the discriminator's role is to distinguish between real and synthetic data. The two networks are trained together, with the generator improving its ability to create convincing synthetic data, and the discriminator improving its ability to differentiate between real and synthetic data.
2. Generation of Synthetic Contracts: Once the GAN is trained, you can use the generator to create synthetic contracts. These contracts will have similar characteristics and language as the original contracts but will not contain any real, sensitive information. The synthetic contracts can be generated to maintain the structural and statistical properties of the original contracts.
3. Analysis with LLMs: Then, you can use an LLM like GPT-3.5 to review and analyze these synthetic contracts. Since these contracts do not contain any real sensitive data, the privacy and confidentiality of the original contracts are preserved.
Generative Adversarial Networks (GANs) can be used to generate synthetic data that preserves the statistical characteristics of the original data without compromising privacy. This approach can be particularly useful in preserving privacy and confidentiality when using Large Language Models (LLMs) like GPT-3.5 for contract review and analysis.
