Solutions for AI Security
Control
- Data Security: Organizations with sensitive or proprietary data building internal employee-facing AI applications are often better served with an on-premises solution that keeps data inside the enterprise.
- Robust Access Controls: Granular permissions for individuals and teams to limit access to models, scanners, and operational metrics and keep costs under control.
- Regulatory Compliance: Features that help organizations stay compliant with proliferating regulatory frameworks for AI use and data privacy that span industry and geo-requirements.
Power
- Rapid Time to Value: Deployment and setup need to be quick and easy, especially when installing on-prem, so organizations can build, test, and release applications sooner.
- GenAI-Powered Defenses: While regex- and neural-net-based guardrails still have their strengths, they don’t come close to the adaptability and contextual awareness offered by GenAI.
- Red-Teaming: Vendor solutions need to offer tools that replicate realistic adversarial attacks so applications can be properly tested before going into production, and whenever new attacks and vulnerabilities are discovered.
Flexibility
- Model Agnostic: Organizations need to be able to select the best public models for their use cases, change models when better ones come along, and use their own internal models and RAG data.
- Customization: No security solution can anticipate every specific use case and risk profile, so organizations need the ability to adjust and fine-tune out-of-the-box controls and attacks.
- Robust API: It goes without saying that every enterprise security product needs a good API enabling data to be pulled into existing workflows.
- Deployment Options: Because every application workflow is different, infrastructure teams want discretion on where AI security tools sit in the tech stack.
