Research Division
Intelligent Systems & AI Safety Division
Advancing safe, interpretable, and governance-aligned intelligent systems.
This division studies how autonomous agents, machine-learning systems, and intelligent workflows operate inside secure, policy-governed infrastructures. It defines frameworks for safe autonomy, verifiable decision-making, and governance-aligned AI.
Core Research Areas
- Agent-native operating models (semantic IPC, memory safety, OS safety)
- AI governance, alignment, and risk controls
- Interpretable decision intelligence & domain-restricted autonomy
- Telemetry for AI oversight (event trails, procedural guarantees)
- AI in regulated ecosystems (finance, industrial, sovereign compute)
Long-Term Objectives
- Establish frameworks for safe AI autonomy in mission-critical environments.
- Define global standards for AI oversight, auditability, and verification.
- Develop agent-native computational models for future intelligent infrastructure.
Intersections with QIST Technologies
Intelligent Systems & AI Safety research guides how QIST deploys agents, learning systems, and automation across regulated and safety-critical domains.
- AIOS
- DDIP Platform
- IACC
- Profy
- WAHH (AI-driven risk & compliance)