Security Architecture
Encryption Standards
Access Control
Business Continuity
Incident Response
Responsible AI Principles
Our commitment to ethical AI is not a policy document — it is embedded in our engineering practices, product design, and team culture.
Transparency
We disclose which AI models power each feature, how they were trained, and what data they use. Users always know when they are interacting with AI-generated content or recommendations.
Fairness & Bias Mitigation
Our models are tested for demographic bias across key attributes before deployment. We maintain bias monitoring in production and retrain models when drift is detected.
Human Oversight
All high-stakes AI recommendations (medical, financial, legal adjacent) include human review checkpoints. Agentic AI actions require human approval for irreversible operations.
Privacy by Design
AI models are trained on anonymised data with differential privacy techniques. We never use customer production data to train general-purpose models without explicit consent.
Explainability
Where technically feasible, our AI systems provide explanations for their outputs — including key factors, confidence levels, and limitations — so users can evaluate recommendations critically.
Accountability
Aivance maintains an AI Ethics Review Board that evaluates new model deployments, investigates AI-related incidents, and publishes annual Responsible AI reports.
Have a Security Question?
For security disclosures, compliance questions, or to request our full security documentation, contact our Security team.