Generative AI is crashing over the enterprise like an enormous wave—and organizations are either going to find ways to ride that wave, or they’re going to be crushed by it. Most enterprises are struggling to stay afloat with the rapid pace of change and technological innovation that is taking place right now.
There is a sense of inevitability about generative AI adoption. However, the risks are very real:
- Inaccurate or nonfactual outputs: LLMs tend to “hallucinate/confabulate” or to generate content that looks and sounds like real information but is, in fact, inaccurate or nonfactual.
- Harmful or values-misaligned outputs: LLMs have the potential to generate biased, toxic, and harmful outputs that can be a major liability for external- or customer-facing applications.
- Leakage of PII and sensitive data: LLMs can “leak” sensitive data like personally identifiable information or company IP that has been included in their training datasets, which makes it critical for companies to control what data gets incorporated back into the training set and to put controls in place to prevent this data from getting leaked in model outputs.
- IP infringement: given that LLMs can reproduce data they have been trained on in their outputs, there is a risk that organizations using LLMs for code or image generation could accidentally use IP-infringing content that has been outputted by a generative AI tool, exposing a company to legal risks.
- Prompt injection attacks: adversarial attacks against LLMs are becoming a major risk, particularly as generative AI systems get connected to APIs or databases. Bad actors can prompt LLMs to process poisoned content from the web that causes the model to expose sensitive data or override the system instructions to produce malicious outputs.
At the same time, generative AI has the potential to completely transform the way that businesses and society operate and create value. Organizations that aren’t finding ways to enable their employees with generative AI tools, or finding ways to incorporate generative AI into their business processes and operations, are going to be left behind.
That’s why today, we’re announcing the general availability of Credo AI’s GenAI Guardrails, a powerful new set of governance capabilities as part of the Credo AI Responsible AI Platform, designed to help organizations understand and mitigate the risks of generative AI so that they can realize its full potential.
GenAI Guardrails: Policy Intelligence Powering Generative AI Safety & Governance
The heart of the Credo AI Responsible AI Platform is the policy intelligence engine—the translation of high-level legal, business, ethical, and industry policies into actionable and operationalized requirements for assessing and governing AI/ML systems.
Today, we are announcing a new set of capabilities that extend Credo AI’s policy intelligence engine to govern and enable the generative AI space: GenAI Guardrails.
All generative AI systems—from ChatGPT to GitHub Copilot—are made up of three primary components: the infrastructure layer, the large language model (either open source or proprietary), and the application layer.
Each layer provides different and important opportunities to implement risk-mitigating controls.
For example, at the application layer, an organization can implement input/output filters that are designed to block potentially risky or harmful model outputs before they reach end-users; in the infrastructure layer, organizations can implement privacy- and security-focused controls that prevent models from interacting with sensitive data, or that prevent prompts and model outputs from leaving an organization’s firewall.
With the release of GenAI Guardrails, you can now track, measure, and mitigate generative AI risks from a centralized governance platform and apply critical controls to your generative AI systems at every layer of the stack.
GenAI Guardrails provides organizations with governance features that support the safe and responsible adoption of generative AI tools, including:
- An AI Registry for centralized tracking use cases for generative AI models and tools across the enterprise, with Credo AI risk recommendations that surface relevant risks specific to GenAI systems based on the context in which they are being deployed and used;
- GenAI-Specific Policy Packs that define out-of-the-box processes and technical controls designed to mitigate the risks of using generative AI for text generation, code generation, and image generation;
- Technical integrations with LLMOps tools that enable governance teams to implement and configure I/O filters, privacy- and security-preserving infrastructure requirements, and other risk-mitigating controls across the GenAI stack from a centralized governance command center;
- GenAI usage and risk dashboards that surface insights about employee use of GenAI tools, so governance teams can quickly identify and mitigate emerging risks as employees experiment and discover new ways to use generative AI to augment their work;
- A GenAI sandbox that wraps around any LLM and provides a secure environment for safe and responsible experimentation with generative AI tools;
Adopt Generative AI with confidence today and protect your organization with Credo's GenAI Guardrails. Request a demo today!
“Our GenAI guardrails mark a new era of responsible and secure adoption of generative AI technologies, empowering users to unlock their full potential while mitigating known risks of this powerful technology. By equipping organizations with essential safeguards starting at the point of use, we're not only accelerating the integration and use of Generative AI into diverse industries but also catalyzing a safer, more reliable AI ecosystem. The GenAI Guardrails reaffirms our commitment to AI safety and Governance, delivering cutting-edge solutions that drive progress while prioritizing safety and ethical considerations. “ - Navrina Singh, Founder and CEO of Credo AI.
