Defaince offers an Adversarial Defense Platform that employs techniques such as adversarial training and input sanitization to protect AI models and datasets from machine learning vulnerabilities. The platform ensures compliance with industry standards while enabling developers to identify and mitigate risks from adversarial attacks, enhancing the integrity and performance of AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
AI models are vulnerable to adversarial attacks that can compromise their integrity, performance, and security. Current security solutions often fail to adequately address the evolving AI threat landscape, leaving AI builders, security teams, and product teams exposed to significant risks.
Solution
Defaince offers an Adversarial Defense Platform designed to protect AI models and datasets from a comprehensive range of machine learning vulnerabilities. The platform employs techniques such as adversarial training, input sanitization, and anomaly detection to identify and mitigate risks from adversarial attacks. By providing end-to-end security compliance, Defaince enables developers to meet AI security and safety standards, including NIST, MITRE's ATLAS, and OWASP LLM Top 10. The platform's automated features and developer-first design simplify the complex process of securing AI systems, ensuring their integrity and performance without compromising on speed or efficiency.
Target Audience
Defaince is designed for AI builders, AI security teams, and AI product teams across startups, scale-ups, and enterprises who need to secure their AI solutions against evolving threats and ensure compliance with industry standards.
Features
- Comprehensive vulnerability detection covering gradient scans, attribute inferences, patch attacks, evasion attacks, GAN attacks, backdooring, model extraction, and more.
- Support for various model types, including LLMs, regression models, and clusterization-based models.
- Automated shielding against novel, zero-day AI vulnerabilities.
- End-to-end security compliance with industry standards like NIST, MITRE's ATLAS, and OWASP LLM Top 10.
- Enterprise-grade security features, including data encryption at REST, TLS for communication, user authentication, and role-based access control (RBAC).
- AI Model Security and Privacy using machine unlearning, zero knowledge machine learning (ZK-ML), fully homomorphic encryption (FHEML), differential privacy (DP) and ensembles methods.
- AI Model Robustness using adversarial training, input sanitization, anomaly detection, distillation, ensembles and model verification.