AI Safety Architecture
Reliable production AI is not the output of a single safeguard. It is the result of model evaluation, runtime guardrails, and governance working together — each layer catching what the one before it missed.
Every model is evaluated before release, every decision passes through a policy engine at runtime, and every action is monitored and logged — so behavior stays inspectable long after deployment.
AI Safety Architecture
Defense layers from model to deployment
AI Models
Safety Evaluation
Risk Assessment
Policy & Guardrails
Human Oversight
Safety Monitoring
Governance & Compliance
Research signals correlated across alignment, evaluation, and policy.
AI Safety
Research Areas
Our research explores the systems and practices required to make advanced AI safer, more reliable, and accountable across the entire lifecycle — from model development to deployment.
AI Alignment
Research into aligning model behavior with intended goals, safety objectives, and human expectations.
Model Safety
Red-teaming, adversarial testing, and behavioral evaluation to identify failures before deployment.
Risk Evaluation
Assessing capability, misuse potential, failure modes, and downstream risks across AI systems.
AI Governance
Safety policies, human oversight, and deployment controls that define how AI systems are released.
Research Topics
Our AI safety research explores the challenges of building, evaluating, deploying, and governing increasingly capable intelligent systems.
Identify AI Safety Risks
Study model capabilities, failure modes, misuse scenarios, and emerging risks to understand where advanced AI systems can behave unexpectedly.
Evaluate Model Behavior
Evaluate models through capability testing, adversarial prompts, red-teaming, and behavioral benchmarks to uncover weaknesses before deployment.
Design Safety Controls
Develop alignment techniques, runtime guardrails, policy controls, and human oversight mechanisms to reduce unsafe or unintended model behavior.
Validate Before Deployment
Validate safety controls through independent review, adversarial testing, and staged evaluation before systems are introduced into production.
Monitor & Improve
Continuously monitor deployed systems for behavioral drift, emerging threats, and unexpected outcomes, using findings to improve future safety controls.
Identify AI Safety Risks
Study model capabilities, failure modes, misuse scenarios, and emerging risks to understand where advanced AI systems can behave unexpectedly.
Evaluate Model Behavior
Evaluate models through capability testing, adversarial prompts, red-teaming, and behavioral benchmarks to uncover weaknesses before deployment.
Design Safety Controls
Develop alignment techniques, runtime guardrails, policy controls, and human oversight mechanisms to reduce unsafe or unintended model behavior.
Validate Before Deployment
Validate safety controls through independent review, adversarial testing, and staged evaluation before systems are introduced into production.
Monitor & Improve
Continuously monitor deployed systems for behavioral drift, emerging threats, and unexpected outcomes, using findings to improve future safety controls.