Enterprise AI Security & Risk Solutions

Use Cases

Quickly vet and onboard new AI

without slowing down innovation

Deploy safe AI systems without being a blocker for business requests.

Detect and respond to unvetted AI use before it has a potential impact on the business.

Eliminate onboarding friction that is often the cause of Shadow AI.

Continuously monitor

AI services, models, users and data

Find homegrown LLM applications, public AI services, and embedded AI features in SaaS applications.

See who is using which AI systems with department-level summary, and individual-level details.

Monitor what information is being uploaded as files or prompts.

Eliminate shadow AI

and standardize on strategic investments

Drive usage and broad adoption of strategic AI investments.

Minimize unvetted and unsanctioned AI Use.

Put policies in place to block use or redirect employees to approved AI systems.

Prevent data leakage

and keep models from training on sensitive data

Keep employees from inputting or uploading sensitive or private data into AI systems that collect and use that data for model training.

See how AI embedded in SaaS applications are configured, and which have AI features turned on by default.

Put policies in place, with a variety of enforcement actions, to keep sensitive data secure.

Reduce AI sprawl

and avoid unnecessary AI spend

Eliminate AI subscriptions that are not being used.

Avoid spending on unsanctioned AI when approved safe alternative exists.

Shut down redundant LLM in your cloud and optimize homegrown applications.

Comply with internal rules

and external regulations

Make sure AI use across the organization complies with both external regulations and internal mandates and policies.

Put policies in place at general level or granular level to address the complete spectrum of usage.

Continually enforce policies and rules to ensure compliance.

Frequently Asked Questions

What does vetting an AI system mean?

New AI systems need to be vetted with risk assessment and policies put in place to ensure Safe AI Use. Typically AI systems and the context in which they are used, determines if AI use is safe. The underlying models and services, settings and key attributes, use case, security profile, user role, and the data input as prompts - should all be considered together, to make a determination of the risk of AI use. When AI systems or a component change, another rapid vetting cycle may be needed to ensure Safe AI Use but without causing any disruption.

Why is rapidly onboarding new AI services so important?

AI adoption is accelerating, and organizations face increasing pressure from boards and executives to leverage AI for efficiency and innovation. Employees are eager to use AI-powered co-pilots and productivity tools to work smarter, not harder. However, when IT and security teams take too long to vet and approve these tools, employees become frustrated and often bypass official processes. This leads to AI sprawl, unsanctioned AI usage, and shadow AI —where employees use personal accounts or unsecured freemium tools, exposing the organization to security risks. Delays also create inefficiencies, resulting in redundant tools within the same category and unnecessary costs. By rapidly vetting and onboarding AI technologies, enterprises can streamline adoption, minimize security risks, and ensure employees have access to approved, secure, and cost-effective solutions. This approach not only reduces bottlenecks but also fosters innovation without compromising security and compliance.

Why is it hard to discover all the information needed to ensure secure and efficient AI use?

There isn't one type of generative AI — public generative AI services and co-pilots, internally developed AI systems, and SaaS applications with embedded AI -- are three distinct vectors of AI adoption in the enterprise. Each of these operates differently, with distinct settings, controls, and ways of handling data. This diversity makes it difficult to consistently track, assess, and govern AI use across an enterprise. Compounding this challenge is the heterogenous way in which these are deployed and consumed — it spans different environments, integrations, and user interactions. Each type presents unique hurdles in discovery and management. To effectively discover AI, organizations need technology that can not only detect and classify all types of AI usage but also supports various deployment models while allowing seamless integration. The complexity of these requirements makes AI discovery and governance a difficult yet essential task.