Our Focus Areas
Discover the disciplines shaping quantum-safe systems across national security.Â
Overview
ATNA-CIPHER’s work is organized around a defined set of focus areas addressing performance, security, and resilience challenges across modern and emerging computing environments. These areas reflect both applied cryptographic deployment and foundational research, supporting systems operating at national, enterprise, and global scales.
Ultra High-Speed Encryption
RDMA · Secure LANs
Encryption architectures optimized for environments requiring extremely low latency and high throughput, including RDMA-enabled networks and secure local area networks.
National Security Algorithms
Cryptographic designs intended to meet the operational, assurance, and resilience requirements associated with national security systems and allied defense environments.
Classified Systems
Encryption approaches suitable for classified and restricted-access systems, emphasizing isolation, integrity, and controlled data movement across secure domains.
STEAM &
Fundamental Research
Exploration of foundational cryptographic concepts and system-level design, supporting long-term research and development.
Financial, Banking, FinTech, and Crypto Systems
Encryption models designed for high-volume transactional environments, addressing performance, integrity, compliance, and evolving threat models.
Databases, File Systems,
and Storage
Cryptographic approaches for data-at-rest protection, including new XTS-mode strategies intended to improve scalability, security, and performance in modern storage architectures.
Format-Preserving Encryption
Alternative format-preserving encryption models designed to extend beyond FF1 and FF3, supporting structured data protection without sacrificing usability or system compatibility.
Emerging Quantum-Resilience Market
Research and development aligned with a newly identified $16B+ year-over-year market, driven by the growing demand for quantum-resilient cryptographic systems.
Quantum Resilience & Transition Periods
Encryption strategies aligned with NIST guidance, addressing transitional periods between classical cryptography and post-quantum systems.
Satellite and Space Communications
atnaCM has design features improvising over existing transport security and authentication for providing customer application higher reliance, better performance including vector forwarding, sort-based routing and multi-lane with track and forward.
Data Center - ACaaS
The solution design provides multi factor costs saving to existing data centers in multiple areas like Cost of Operations implying reduced power requirement and Allocated Cryptography as a Service (ACaaS) to permit better cryptographic service scaling including a bootstrap service guarantee model for Data Center instances and reducing dead zone power requirements.
Codesign Architecture
The ATNA-CIPHER architecture is built through coordinated hardware, software, firmware, and QC co-design, enabling encryption to operate as an integrated system component rather than an external overlay supporting a unified cross-compatible multiprocessing model.
Instruction-Set-Based ASIC Architecture
Cryptographic operations are defined at the instruction-set level, supporting a superlative parallel instruction set for boosting high performing computing using ASIC and CPU/GPU accelerator implementations.
Next-Generation Networking Integration
Support for RDMA and secure LAN environments enables encrypted data movement without introducing prohibitive latency or throughput penalties and also next generation disruptive ingress and egress TCAM architectures.
While supporting any networking layer, it facilitates a research R&D model for alternative designs to the existing TCP/IP and ISO networking stack models, like eliminating some performance hindering limitations and the need for up-layer propagation for down layer traffic.
AI-Scale Compute and Farm Bridging
Designed for large-scale distributed AI environments, the architecture supports secure bridging across compute farms with more than 32,000 nodes. This enables protected model training, distributed workload execution, and high-density data movement within advanced AI computational ecosystems.