Security
Research articles and engineering insights on AI security, cybersecurity, identity systems, infrastructure protection, and secure AI deployment.
Secure Core
Data & workloads protected end-to-end
Perimeter Defense
Firewalls & WAF
Identity & Access
IAM & Auth
Monitoring
Logs & Alerts
Compliance
SOC 2 & Audit
Encryption
AES-256 & TLS
Isolation
Network Segments
Security built into every layer.
Research across cybersecurity, identity, infrastructure protection, threat detection, and secure systems engineering.
Research and engineering focused on resilient systems, secure architectures, and defense-in-depth protection.
Secure identity systems, authentication, authorization, and least-privilege access controls.
Research into threat intelligence, anomaly detection, attack surfaces, and emerging threats.
Encryption, secure data handling, privacy-aware architectures, and protection for sensitive workloads.
Security architectures built around continuous verification, isolation, and explicit trust boundaries.
Security telemetry, monitoring, logging, and detection systems across modern infrastructure.
Security controls, auditing, governance, and assurance practices for system-level security.
Research into hardened infrastructure, network security, workload isolation, and secure deployments.
Secure AI systems are assembled layer by layer, not hardened afterwards.
A request reaching an inference service crosses several trust boundaries before a single token is generated. Identity is resolved at the edge, the gateway normalises and rate-limits the call, and policy controls determine what the caller may reach.
Every boundary emits evidence. Authorisation decisions, prompt and tool metadata, model versions, and retrieval sources become part of an append-only audit path that feeds continuous monitoring.
Each boundary produces verifiable security evidence before a request reaches the model-serving layer.
Request path through the enforcement layers of a production AI service.
Research topics
Each topic below is an active line of work with published internal findings, reference configuration and open questions. Summaries are short by design; the expanded text states the current position and where it is still weak.
We treat the model as an untrusted transformer of text. Instructions arriving through retrieved documents, tool responses or user content are never granted the authority of the system prompt. Work here covers content provenance labelling, output constraint checking before side effects, and isolation of tool execution so a successful injection cannot escalate beyond the caller's own scopes.