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In the ever-evolving landscape of artificial intelligence (AI), models, including but not limited to large language models (LLMs), have emerged as powerful, transformative tools with the potential to revolutionize the way we work within the AI security field and beyond. Thanks to this broad spectrum of cutting-edge technologies, it has never been easier for organizations to foster cross-functional collaboration.

The Power of Generative AI Models

Generative AI (GenAI) models have captured the imagination of AI enthusiasts and practitioners worldwide due to the models’ impressive capabilities. While LLMs like ChatGPT and others are prominent examples of models that rely on natural language processing (NLP) to produce textual content, other types of generative models can produce valuable non-text outputs. For instance, models such as Midjourney can produce images from spoken prompts and specialized generative adversarial networks (GANs) can generate synthetic data, such as satellite or medical imagery. Generative models can execute an unfathomably wide range of tasks, which makes them invaluable tools for many industries.

Enabling Secure Cross-Functional Collaboration

GenAI models can break down silos within organizations and promote cross-functional collaboration in innovative ways:

  • Cross-functional teams can automate content creation by using team-created and team-curated natural language prompts that provide clear, on-point messaging and appropriate structure for documentation ranging from emails and policies to wikis, libraries, and knowledge bases. This saves countless hours, leaving the team members more time to focus on strategy and other topics and tasks, thus enhancing productivity and enabling efficient resource allocation.
  • Streamline brainstorming sessions with experts from different fields by enabling them to propose ideas and validate hypotheses, products, or services that can be developed and tweaked in real time, leading to creative solutions with lower R&D costs. Additionally, voice-to-image and text-to-image models further accelerate teams’ ability to create prototypes of slide decks, infographics, images, and other graphical elements quickly and easily.
  • Using pre-trained or fine-tuned models on data from different departments to facilitate shared knowledge can be leveraged to ensure alignment across the enterprise.
  • Language barriers that typically exist in organizations operating in different geographies are eliminated by LLMs’ ability to provide real-time translation between myriad languages. Creating an environment with seamless communication promotes inclusivity and easy collaboration among diverse team members. 
  • Teams can leverage LLMs to aggregate and analyze large datasets and generate insights from different perspectives for informed, data-driven decision-making. LLMs make it easy to retrieve, review, dissect, and manipulate data in real time. Scenarios and other predictive analyses can be generated rapidly, allowing teams to assess the risks, benefits, outcomes, and downstream consequences of each scenario or segment thereof.

Aligning Security and Productivity

Security concerns can be magnified or multiplied when teams from different regions and business functions need access to the same content, but our model-agnostic LLM security solution, Moderator, can alleviate those concerns by enabling access via secure APIs created for individual or team use. Moderator also provides group leaders the ability to form teams using policy-based access controls (PBAC), which means that participation on the team is restricted to specific personnel and access is limited to admin-selected models that can range from large external models, such as ChatGPT, targeted models, such as BloombergGPT, models embedded in SaaS applications, as in Salesforce, retrieval-augmented generation (RAG) models that are trained for specific tasks, to small, internal models fine-tuned on proprietary company data. Additionally, usage of the models can be bounded by admin-determined rate limits and tracked by both team and individual users. 

Collaborative activities have never been more protected from internal and external threats. Moderator provides a transparent, secure, “weightless” layer insulating LLM use from potential risks across the enterprise. All prompts and responses are channeled through detailed scanners in real time to ensure alignment with company Acceptable Use and other policies, and to ensure private and proprietary data are not being sent out of the organization and malicious or otherwise damaging content is not able to enter the organization’s system. All interactions are retained in a comprehensive prompt history archive to facilitate auditing and visibility. The archive can be purged manually or on an automated cadence.   

Long-Term Benefits

GenAI models, including large and small, open-source and proprietary, unimodal and multimodal, and other types, represent a diverse array of tools that can nurture and empower cross-functional, collaborative teams as no other tools have done. The models can generate output ranging from text, audio, video, and images to data-driven insights and simulated environments, making them valuable assets across many industries and applications. Broader benefits of deploying LLMs to meet such use cases include holistic problem solving, faster innovation, improved decision-making, enhanced productivity, rapid prototyping, and improved user experiences, all of which are key drivers of success in today’s competitive environment.  

By embracing these capabilities, organizations can harness the transformative power of GenAI as a catalyst for driving innovation, solving complex problems, and making data-driven decisions in a safe, secure, protected ecosystem.