Noma is a security technology platform that provides real-time monitoring and protection for the entire Data and AI lifecycle, including development environments, data pipelines, and AI models. It addresses vulnerabilities and compliance risks by offering automated security assessments, continuous visibility, and actionable remediation across diverse data and AI infrastructures.
Funding
$132M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CCDVSFounders
Product
Problem
Application security (AppSec) teams lack visibility into the security risks associated with data and AI lifecycles, including development environments, data pipelines, and AI models. Existing security solutions often fail to address vulnerabilities, misconfigurations, and compliance risks specific to data and AI infrastructure, leaving organizations exposed to new threats like data leakage and model manipulation.
Solution
Noma provides a Data and AI Lifecycle Security platform that delivers real-time monitoring and protection across the entire AI development and deployment process. The platform offers automated security assessments, continuous visibility, and actionable remediation for vulnerabilities and compliance risks within diverse data and AI infrastructures. Noma strengthens AI security posture by identifying misconfigurations and vulnerabilities early in the development cycle, while also providing runtime protection against adversarial attacks and data leakage. By mapping data pipelines, notebooks, MLOps tools, and AI components, Noma generates a comprehensive AI/ML-BOM, enabling AppSec teams to proactively improve their AI security posture and stay ahead of emerging AI regulations.
Target Audience
Noma is designed for application security teams, CISOs, and security leaders in enterprises, particularly those in regulated industries, who need to secure their data and AI lifecycles from development to production.
Features
- Automated scanning of Jupyter Notebook environments for exposed secrets, code risks, sensitive data, and misconfigurations
- Mapping of data pipelines, jobs, model registries, and model serving tools to detect misconfigurations and vulnerabilities
- Detection of malicious and vulnerable open-source models and datasets within registries and across environments
- Automated AI red teaming to statically and dynamically test models for AI vulnerabilities and risks pre-production
- AI threat detection and response to detect and mitigate AI adversarial threats such as prompt injection, model jailbreak, and sensitive data leakage
- AI safety guardrails to configure and enforce policies that prevent production models from violating organizational content and safety guidelines
- Automated generation of AI/ML-BOM to provide a comprehensive inventory of all supply chain assets, models, and their connected data and tools
- AI data governance to control training data usage and RAG access, ensuring customer data isn't used for training or leaked