AI, Data & Intelligence
AI Privacy & Data Protection
Privacy by design for AI systems: lawful data use for training and inference, DPDP Act and GDPR obligations, data minimisation, retention, vendor terms and handling of individuals' rights when AI is involved.
Capability overview
What ai privacy & data protection involves
AI projects stretch data protection practices. Historical customer data collected for one purpose is proposed for model training, prompts containing personal details are sent to overseas model providers, and individuals may ask how an automated decision about them was made or request that their data be erased from a trained model.
We embed privacy requirements from India's Digital Personal Data Protection Act 2023 and its Rules, and the GDPR where European data is involved, into AI design. That includes purpose and consent checks, impact assessments, minimisation techniques such as pseudonymisation, cross-border transfer review and processes for data principal requests.
What is included
Privacy workstreams
Purpose and consent review
Whether existing notices and consents cover proposed AI training and use, and what changes are needed if they do not.
Privacy impact assessment
Structured assessment of AI processing risks to individuals with mitigations, suitable for data protection officer sign-off.
Data minimisation techniques
Pseudonymisation, aggregation, synthetic data and redaction applied before data reaches training pipelines or external providers.
Vendor and transfer review
Model provider terms on retention, training use, subprocessors and data location checked against your obligations.
Rights handling
Processes for access, correction, erasure and grievance requests that involve AI systems, including explanations of outcomes.
How we work
How we deliver ai privacy & data protection
Data flow mapping
Personal data traced from collection through training, retrieval, prompts, logs and outputs.
Obligation analysis
Applicable laws, contracts and sector rules identified for each flow.
Design adjustments
Architecture and process changes agreed with engineering to reduce privacy risk before launch.
Documentation
Notices, records of processing, assessments and vendor agreements updated.
Operational checks
Log retention, access reviews and request handling tested after go-live.
Related capabilities
Related capabilities in Responsible AI & AI Security
Bias & Fairness Assessment
Measurement of whether an AI system produces unequal outcomes or error rates for different groups of people, with statistical analysis, root cause investigation and practical mitigation options.
Model Explainability
Techniques and interfaces that show why an AI model produced a particular prediction or decision, for data scientists debugging models, staff reviewing cases, regulators and the customers affected.
AI Model Monitoring
Continuous monitoring of production AI models for data drift, accuracy decay, fairness shifts, unusual outputs and operational health, with thresholds that trigger review, retraining or rollback.
AI Compliance Readiness
Preparation for AI-specific regulations and standards, including EU AI Act obligations for organisations serving European markets, ISO/IEC 42001 certification and sector regulators' expectations in India.
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Questions & answers
Questions about AI Privacy & Data Protection
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionIt depends on the purpose communicated when data was collected, the consent or legal basis relied on and the sensitivity of the data. Anonymisation or synthetic data can sometimes avoid the issue, and the review gives a documented answer.
Often yes, subject to the applicable transfer rules, contractual safeguards and any sector-specific localisation requirements. Some regulated data may need processing in India or self-hosted models.
Removing specific data from a trained model is technically difficult. Practical approaches include excluding it from future retraining, filtering outputs and designing systems so personal data sits in retrievable stores rather than model weights.
Assessments are fixed price per AI system. Broader programme work, such as updating notices and vendor contracts across many systems, is quoted as a project.
Next step
Discuss ai privacy & data protection with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.