CYNAERA Security Architecture
Zero-Data, Zero-Cloud, Zero-Risk Intelligence
CYNAERA’s security architecture is built on one principle. The safest intelligence system is the one that collects the least data, exposes the fewest surfaces, and depends on no external infrastructure. Instead of relying on cloud services, GPU clusters, or persistent datasets, CYNAERA operates as a deterministic, logic-driven platform with near zero external dependencies. This minimizes risk and long-term cost while enabling rapid, compliant deployment across hospitals, public health systems, government agencies, and disaster response environments. CYNAERA is engineered for the future of ethical AI, clinical stability, and government-grade trust.
No Cloud Dependency Means No Cloud Exposure
Most AI companies sit on top of cloud providers that introduce risk through multi-region replication, shared compute, third-party access pathways, and vendor-side logs. CYNAERA eliminates this entire category of vulnerability. The system does not rely on remote compute, cloud inference pipelines, cloud storage, or external training environments. There are no cloud-based logs, no shared memory systems, and no multi-tenant workloads that can leak data. With no cloud dependency, CYNAERA avoids the vulnerabilities that have become routine across the industry. Hospitals, agencies, and public health departments gain a safer and more predictable platform with no exposure to cloud compromise.
No GPU Clusters: A Smaller Attack Surface
GPU clusters often store model weights, logs, cached data, and multi-tenant workloads. They are high-value targets for attackers and introduce complex risks. CYNAERA does not use GPU inference, GPU accelerated training, GPU based storage, or GPU cloud pipelines. This reduces operational complexity and sharply limits the attack surface. The platform exposes no model weights, no retraining artifacts, and no compute workloads that can be intercepted or manipulated.
No Stored Datasets: Privacy by Architecture
CYNAERA does not store patient data, user data, environmental histories, health records, or institutional logs. It processes only what it needs, only when it needs it, then discards the information immediately. There is no database to breach, no archive to subpoena, no long-term storage liability, and no risk of dataset poisoning or inference leakage. This design is inherently compliant and avoids the retention burdens found in HIPAA, GDPR, and large-scale AI archives. Healthcare systems and government agencies gain the confidence that CYNAERA creates no permanent exposure.
No Inference Logs: Nothing to Steal and Nothing to Trace
Most AI systems log every interaction. These logs create hidden records containing prompts, outputs, identifiers, session metadata, and error traces. They are valuable targets for attackers.
CYNAERA generates no inference logs. No transcripts. No saved prompts. No stored metadata.
This protects:
• patient populations
• disaster zone operations
• protected health information
• high sensitivity institutional workflows
• communities vulnerable to surveillance
With nothing logged, the risk surface disappears.
Deterministic Logic: Security Through Predictability
CYNAERA is built on static deterministic logic. This is the opposite of machine learning systems that shift, drift, and change over time.
Neural networks evolve. Deterministic logic does not. Neural networks can degrade. Deterministic logic remains stable. Neural networks are difficult to audit. Deterministic logic is fully explainable. CYNAERA is easier to certify, validate, and verify. It is immune to model drift, resistant to data poisoning, and reliable under security scrutiny. Deterministic systems are the standard in mission-critical environments where failure is unacceptable.
Minimal Attack Surface: Engineered for High-Risk Settings
Every architectural decision supports one goal. Reduce the number of things that can go wrong.
CYNAERA avoids:
• third-party API dependencies
• external model parameter files
• shared memory systems
• persistent user sessions
• stored model states
• external telemetry
This creates a compact security profile with almost no lateral movement potential for attackers. Very few AI systems can provide this level of containment, especially in healthcare, government, and infrastructure contexts.
Ready for Government, Clinical, and Public Health Deployment
CYNAERA’s architecture naturally fits the demands of:
• hospitals
• state health agencies
• emergency operations
• federal resilience programs
• disaster response teams
• chronic illness clinics
• environmental monitoring networks
These sectors cannot risk data leaks, cloud failures, inference logs, or model drift. CYNAERA provides a stable, predictable intelligence layer built for environments where reliability is non negotiable.
Security Is Not a Feature. It Is the Foundation.
CYNAERA is built security on a simple truth. The safest AI is the one that never exposes more than it must. The platform protects patients, shields institutions, and delivers intelligence without relying on fragile infrastructure or vulnerable data pipelines. It is engineered for a world where privacy, safety, and resilience are essential. This is zero-data intelligence built for the next generation of public health and government-grade AI.
About the Founder
Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with Selin Lab for t cell research at the University of Massachusetts.
Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.
Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed perspective to her research and policy initiatives.