Our Mission: Secure AI for Everyone
Building artificial intelligence systems that organizations can trust, deploy confidently, and explain clearly to stakeholders.
Our Story
cypherl ar was founded in 2017 by a team of AI researchers and security specialists who recognized a growing challenge: organizations were adopting artificial intelligence systems without adequate security measures or compliance frameworks. The potential for data breaches, privacy violations, and regulatory penalties was significant.
Our founders had worked together at leading technology companies and research institutions across Singapore. They understood both the technical capabilities of AI systems and the security requirements of enterprise deployment. They saw opportunities to build AI solutions that were secure by design rather than secured as an afterthought.
From our base in Singapore's Central Business District, we began working with financial services firms, healthcare organizations, and government agencies. Each project taught us more about the practical challenges of secure AI deployment. We developed methodologies that balanced security requirements with operational needs and performance expectations.
Today, cypherl ar serves clients across multiple sectors and jurisdictions. Our team has grown to include specialists in cryptography, differential privacy, federated learning, and regulatory compliance. We continue to refine our approaches based on emerging threats and evolving requirements.
Our Values
Security First
We design systems with security as a foundation, not an addition. Every architectural decision considers potential vulnerabilities and protection mechanisms.
Transparency
We explain our recommendations, document our approaches, and help teams understand both protections implemented and trade-offs involved.
Practical Balance
We understand that security measures must work within operational realities and budget constraints. Our solutions balance protection with usability.
Continuous Learning
The threat landscape evolves constantly. We maintain current knowledge of emerging risks, new attack vectors, and developing regulatory requirements.
Our Team
Our specialists combine academic research backgrounds with practical implementation experience across industries.
Dr. Rachel Chen
Chief Technology Officer
PhD in Machine Learning from NUS. Previously led AI research at a major financial institution. Specializes in adversarial ML and model security.
Marcus Koh
Head of Security
Former cybersecurity consultant with 12 years experience. Certified in information security and privacy protection. Leads threat modeling initiatives.
Sarah Lim
Compliance Director
Legal background with focus on data protection and AI regulation. Tracks regulatory developments across multiple jurisdictions. Advises on PDPA compliance.
Quality Standards & Protocols
Security Certifications
Our team maintains current certifications in information security, privacy protection, and AI governance frameworks.
- ISO 27001 Compliance
- PDPA Certification
- AI Ethics Guidelines
Development Methodology
We follow secure development practices that integrate security testing throughout the implementation process.
- Threat Modeling
- Security Reviews
- Vulnerability Testing
Data Protection
Client data and project information receive multiple layers of protection including encryption and access controls.
- End-to-end Encryption
- Access Logging
- Secure Infrastructure
Technical Expertise
Our team brings together knowledge from multiple disciplines that intersect in secure AI development. We understand machine learning architectures, security protocols, privacy-preserving techniques, and regulatory frameworks. This combination allows us to address the full scope of challenges organizations face when deploying AI systems.
In cryptography, we work with techniques like homomorphic encryption that enable computation on encrypted data. We implement differential privacy mechanisms that protect individual information while preserving statistical utility. Our federated learning implementations allow collaborative model training without centralizing sensitive data.
For adversarial robustness, we test systems against known attack patterns and help teams build defenses. We evaluate models for vulnerability to input perturbations, backdoor attacks, and extraction attempts. Our security assessments identify weaknesses before deployment.
Compliance work requires understanding of legal requirements across jurisdictions. We track developments in data protection law, AI-specific regulations, and sector requirements. Our documentation supports audit preparation and demonstrates regulatory adherence.
Explainability techniques vary by application and audience. We select approaches based on regulatory requirements, user needs, and technical constraints. Implementation might involve inherently interpretable models, post-hoc explanation methods, or visualization tools.
Ready to Work Together?
Contact our team to discuss how we can help secure your AI systems.
Get in Touch