Ethical AI & Data Governance That Earns Trust
Practical frameworks for privacy, cybersecurity, and responsible AI — from policy to implementation — to build organizational trust & safety.
Our Approach: People • Purpose • Process
People
Because AI decisions affect lives, we prioritize safeguards for customers, employees, and communities — reducing bias, misuse, and privacy harm.
Purpose
Responsible AI preserves trust and your license to operate: align innovation with GDPR/CCPA/HIPAA and emerging AI rules.
Process
A governed lifecycle keeps AI safe at scale: Readiness → Policy & Controls → Security & Monitoring → Continuous Oversight And Auditability.
Where AI Programs Slip (and How We Fix It)
What we commonly see
- Shadow AI and unclear ownership; no defined responsible AI policy or council.
- Data lineage undocumented; missing DPIAs/impact assessments.
- Incomplete model documentation; no explainability or bias testing.
- Vendor AI without DPAs/DTIAs; unclear usage guardrails for staff.
- Weak access controls, secrets handling, and monitoring for AI pipelines.
How we fix it (People • Purpose • Process)
- People: Cross-functional AI council (product, data, legal, security); roles/RACI; targeted training.
- Purpose: Responsible AI policy, risk taxonomy, privacy/legal basis; traceability across use cases.
- Process: Use-case intake → DPIA/DTIA → model cards & approvals → monitoring & incidents → audits.
Why This Matters Now
AI is everywhere — and so are expectations for accountability. From privacy to bias and explainability, stakeholders and regulators want proof your systems are safe, fair, and secure. We help you innovate responsibly with governance that scales across teams and products.
What We Solve
Responsible AI Program Setup
Stand up policy, council, risk taxonomy, and approval workflows.
Privacy by Design & DPIA
Embed GDPR/CCPA/HIPAA-aligned privacy assessments into AI delivery.
Data Governance
Catalogs, lineage, retention, and data minimization across the lifecycle.
Model Risk & Accountability
Model cards, explainability, bias testing, and approvals.
Security for AI Systems
Pipeline security, access & secrets management, monitoring and incident playbooks.
What We Deliver
AI Ethics Readiness Assessment
- Map current and planned AI use
- Identify risks, bias points, and governance gaps
- Prioritized roadmap for safe scale-up
Data Privacy & Protection
- Policies and DPIAs aligned to GDPR/CCPA/HIPAA
- Data lifecycle controls and retention
- Breach response readiness
Cybersecurity for AI Workloads
- Model and data pipeline security
- Access and secrets management
- Monitoring and incident playbooks
Algorithmic Governance
- Model documentation and approvals
- Explainability and fairness protocols
- Human-in-the-loop checks
Training & Enablement
- Leadership briefings
- Engineer and analyst workshops
- Usage guardrails for vendor AI tools
- Trust & safety playbooks for product and operations
Industry Applications
FAQs
Do we need an AI policy if we only use vendor tools?
Yes — usage still carries risk. Governance defines approved use, data handling, and human oversight.
Will this slow down innovation?
Good governance accelerates delivery by avoiding late-stage rework and regulator pushback.
Can you work with our engineers and legal team?
Yes — our model is a peer-to-peer partnership across functions.
Ready to move responsibly?
Let's align your AI roadmap with privacy, security, and accountability from day one.
Talk to Ethixera