Design and Deploy AI Solutions with Governance Built In
We design governance frameworks that keep deployed AI auditable, compliant, and aligned with enterprise risk standards.
Improvement in Compliance
Risk Incidents
Cost Savings
Get Started with AI Governance Solutions
Building AI That is Powerful, Trusted and Sustainable
Every model we govern is built on data integrity, ethical compliance, and security transparency from day one.
Data Integrity
- Data lineage tracking from source to model output
- Quality checks across training and inference data
- Access controls and versioning for every dataset in use
Ethics & Compliance
- Track all data changes across the workflow.
- Document decision logic for traceable accountability.
- Model impact assessments before deployment
Security & Transparency
- Model registry with full audit trail across production
- Align practices with GDPR, HIPAA, EU, and SOC 2
- Explainability reports that consistently hold up in audit
AI Governance Tailored to Your Risk
Our governance frameworks are designed around the specific risks your AI deployment carries.
Responsible AI Frameworks
- Risk tiering with clearly defined accountability and ownership
- Documentation aligned with regulatory expectations
- Operational monitoring with robust exception handling
AI and Model Validation
- Pre-deployment validation for regulated AI systems
- Rigorous post-change audits after AI model updates
- Independent validation for inherited legacy AI systems
Compliance (GDPR, EU)
- GDPR compliance for automated decision-making
- Full EU AI Act conformity and compliance for high-risk systems
- Technical documentation and registration for compliance
Bias and Fairness Auditing
- Pre-deployment bias across protected attributes
- Outcome-level fairness and impact measurement
- Ongoing monitoring with remediation guidance
LLMOps Governance
- Robust prompt injection protection with output filtering
- Continuous hallucination monitoring with detailed audit logging
- Model version control with secure access management
AI Policy and Guardrails
- Acceptable use policy drafting for AI systems
- Robust technical guardrails with advanced content filtering
- Defined escalation procedures with periodic review cadences
Success Stories: AI Implementation Across Verticals
Explore how we have used our AI governance frameworks to solve real enterprise challenges.
60% Faster Invoice Processing with Intelligent Automation by FLIP
Impact:
- 75% Reduction in Manual Effort
- 90% Data Extraction Accuracy
- 55% Faster Invoice Processing
50% Faster Pricing with AI Dynamic Pricing for Luxury
Impact:
- 24% Increase in Profit Margins on Top SKUs
- 39% Faster Price Change Cycle Time
- 100% Auditability of Pricing Decisions
95% Accuracy in Counterfeit Detection with AI Vision
Impact:
- 95% High Accuracy in Counterfeit Detection
- 68% Faster Product Verification
- 100% Complete Product Traceability
Our Governance Framework
Kanerika's IMPACT framework drives every AI governance engagement, tying compliance controls to business outcomes you can measure.
Tools and Technologies
We build AI governance frameworks that keep enterprise AI auditable, compliant, and in control.
INNOVATE
Diverse Industry Expertise
Empowering Alliances
Our Strategic Partnerships
The pivotal partnerships with technology leaders that amplify our capabilities, ensuring you benefit from the most advanced and reliable solutions.
Frequently Asked Questions (FAQs)
01What is AI governance and why does it matter now?
AI governance is the set of policies, processes, and technical controls that determine how AI systems are built, deployed, monitored, and held accountable. It matters now because the cost of getting it wrong has changed. Regulatory exposure under GDPR and the EU AI Act is real. Model failures in BFSI and healthcare carry direct liability. And organizations that can’t demonstrate governance are finding it harder to win enterprise customers who now ask for it in procurement.
02What is the EU AI Act and what does it require from us?
The EU AI Act classifies AI systems by risk — from minimal to high-risk — and imposes obligations on high-risk systems: conformity assessments, human oversight mechanisms, technical documentation, and registration in a public database. It applies to any organization deploying AI that affects EU residents, regardless of where the organization is based. Phased enforcement began in 2024. Kanerika maps your systems against the Act’s classification framework and builds compliance programs for those in scope.
03When should an organization run an AI audit?
Before deploying AI in a regulated environment. After significant model changes or retraining. When inheriting AI systems through acquisition. And on a defined periodic schedule for high-stakes systems already in production. In BFSI, healthcare, and insurance, periodic AI audit is increasingly an expected practice — not an exceptional one. Waiting for a regulatory review to be the first external assessment of a production AI system is a significant risk.
04What does bias detection actually test?
Bias testing evaluates whether an AI model produces systematically different outcomes for different demographic groups — defined by attributes such as age, gender, ethnicity, or other protected characteristics. This includes statistical parity testing, outcome-level fairness analysis, and disparate impact measurement. Kanerika’s bias work covers pre-deployment validation, post-deployment monitoring, and remediation guidance. A model that passes bias testing at launch can develop bias as its training data drifts — which is why ongoing monitoring matters.
05What is LLMOps governance and why is it different from standard MLOps?
Standard MLOps governance focuses on model accuracy, performance drift, and retraining pipelines. LLMOps governance adds controls specific to large language models: prompt injection detection, output filtering, hallucination monitoring, token usage management, and audit logging of model inputs and outputs. LLMs fail in ways that traditional ML monitoring tools were not built to detect — and those failure modes carry compliance and reputational consequences that are orders of magnitude faster to spread than a miscalibrated regression model.
06How does the IMPACT framework apply to AI governance specifically?
IMPACT — Identify, Map, Prove, Analyze, Create, Transform — ensures that governance work connects to business outcomes rather than existing as a compliance exercise in parallel with actual operations. Identify surfaces governance gaps before they become audit findings. Prove tests governance controls on highest-risk systems first. Analyze quantifies the cost of governance failures against the cost of prevention. Transform embeds governance into ongoing AI operations — not as a separate program, but as the operating standard.
07What is model validation and how is it different from testing?
Model testing is conducted by the development team against predefined test cases before deployment. Model validation is an independent assessment — accuracy, bias profile, data quality, and regulatory compliance — conducted by a party separate from the development team. The distinction matters in banking and insurance where model risk management frameworks require independent validation before a model enters production. Internal testing does not satisfy that requirement.
08How does Kanerika's governance work connect to its AI development practice?
For organizations working with Kanerika on AI application development, governance requirements are captured in discovery and designed into the architecture from day one. This is not a separate workstream. It determines data handling decisions, access control design, audit logging scope, and the monitoring infrastructure built into production deployment. Governance added after the fact is a retrofit. Governance built in from the start is an architecture.
AI Governance That Holds Up Under Scrutiny
Talk to a Kanerika AI governance expert and get a free assessment of your model risk, compliance gaps, and audit readiness.