Detect, Classify, Remediate Manipulative Journeys Before
Regulators Intervene
Motherson Technology Services Dark Pattern Detection scans customer journeys, source code and API traffic to find manipulative design before it becomes a compliance issue. Every result is mapped against the relevant regulation, backed by evidence, and paired with clear guidance on what to fix.
A dark pattern is a design choice built to steer a customer towards an outcome they did not intend: an unwanted purchase, an unnecessary disclosure, or agreement to terms they never properly saw. The Central Consumer Protection Authority (CCPA) has mandated such practices.
Countdown timers, low-stock warnings and similar pressure tactics used to force a rushed decision
Products or add-ons placed in a customer's cart without their explicit consent
Wording designed to guilt a customer into staying on a particular option
Requiring registration, subscription or an unrelated step before a customer can proceed
Fees or charges that only appear once the customer has committed to the purchase
A sign-up flow that takes seconds, paired with a cancellation flow that is deliberately hard to find
Visual design choices that highlight one option while burying another
Advertising one product, price or outcome, then delivering a different one at checkout
Paid promotional content presented as an independent review or organic content.
Repeated prompts or pop-ups used to wear a customer down into a particular choice.
Recurring charges structured so renewal is automatic and cancellation is difficult to locate
Fake security alerts used to push a download or a purchase.
Opt-in or opt-out language written to be misread or misunderstood.
MTSL Dark Pattern Detection is an AI-powered compliance platform that identifies, classifies and reports dark patterns across an organisation's digital estate. It replaces periodic manual reviews with repeatable, evidence-backed detection, across the interfaces customers see, the code behind them, and the APIs that move data between the two.
Automated Detection - AI models analyse digital journeys and flag patterns that need a compliance review
Regulatory Classification - Every flagged pattern is mapped to the relevant regulatory category and journey stage, with a confidence score attached
Risk & Severity - Findings are organised by severity, so teams know what to prioritise first
Remediation Guidance - Specific, actionable direction for product, UX, technology and compliance teams on how to close each finding
Evidence Packs - Screenshots, findings and audit trails, captured into a record built for review and reporting
Self-Audit Dashboard - One consolidated view of findings, severity, trends and remediation progress for the compliance team
Proof-of-concept Results Based on the Current Solution Scope
Scan - Analyse selected websites, applications, repositories or API payloads
Detect - Identify potential dark patterns across the configured categories
Classify - Map each finding to the relevant regulatory category, with confidence and severity assigned
Review - Present findings on a dashboard, with supporting evidence and context
Remediate - Give product, UX, compliance and engineering teams clear, actionable guidance
Report - Generate evidence-backed findings and audit trails for internal governance and compliance review
Review product discovery, pricing, cart and checkout journeys for potentially manipulative practices
Continuously assess digital storefronts and customer journeys as products and experiences evolve
Review subscription, renewal, cancellation and billing experiences for misleading or restrictive practices
Assess regulated digital journeys where consent, suitability, product selection and communication need closer scrutiny
Digital consumer journeys are under growing regulatory scrutiny. The platform helps organisations assess their digital experiences against applicable dark-pattern and responsible-business-conduct requirements.
Supports detection across the dark-pattern categories set out in the Guidelines for Prevention and Regulation of Dark Patterns, 2023
IRDAI has directed insurers and intermediaries to comply with the CCPA dark-pattern guidelines and to self-assess their digital journeys. Findings are mapped across insurance-specific journey stages to support that assessment
Supports assessment of banking-specific customer journeys involving consent, product suitability, bundling, agent conduct and mis-selling controls, ahead of the Responsible Business Conduct (Second Amendment) Directions, 2026, taking effect on 1 January 2027
Consent by Design - Assess whether consent mechanisms are explicit and properly separated across products
Mis-Selling Controls - Identify journey characteristics that may contribute to inappropriate product selling or outcomes
Bundling - Assess whether third-party products or services are presented in ways that could improperly restrict customer choice
Agent Governance (DSA / DMA) - Support review of journeys involving direct selling agents, direct marketing agents, sub-agents and other channels now within regulatory scope
Suitability - Assess journey controls around customer suitability and product fit
Customer Contact Controls - Support review of applicable consent and interaction controls across customer journeys
The managed deployment runs on AWS services chosen for scalable capture, orchestration, AI-powered detection, evidence management, identity and operational visibility.
Capture - Amazon ECS / AWS Fargate, AWS WAF and Elastic Load Balancing support scalable web capture and controlled ingress
Orchestration - Amazon EventBridge, Amazon SQS and AWS Step Functions coordinate scheduled and event-driven scanning workflows
AI & Detection - Amazon Bedrock and/or Amazon SageMaker support managed inference, with Amazon OpenSearch supporting retrieval and pattern knowledge
Data & Evidence - Amazon S3 stores screenshots and evidence; Amazon Aurora holds cases, findings and configuration data
Identity & Access - Amazon Cognito supports authentication and role-based application access
Security & Operations - AWS KMS, AWS Secrets Manager, AWS IAM, AWS CloudTrail, Amazon GuardDuty, Amazon CloudWatch and AWS Config support encryption, access control, auditability, monitoring and governance
This gives product, engineering and compliance teams a way to manage dark-pattern risk across the entire delivery lifecycle, rather than relying solely on periodic manual audits.
Scan customer-facing web and
application journeys
Analyse repositories to identify
risky implementation patterns
earlier
Inspect application behaviour
that isn't obvious from the visual
interface alone
Consolidate findings, screenshots,
severity and remediation guidance
in one place
Motherson Technology Services pairs AI-led detection with enterprise technology delivery, helping organisations put dark-pattern compliance into practice across complex digital estates; not just identify it on paper.
The platform is built to bring compliance, product, UX and engineering teams into a single workflow: surfacing issues, providing evidence, prioritising remediation and supporting ongoing governance.
Validate the platform against selected digital journeys before committing to a broader deployment.
Crawler-based detection across selected web, responsive, iOS and Android experiences
Run the full supported dark- pattern set, or a subset chosen for the use case
Findings organised by surface, journey step and category, ready for review and remediation
Assess multiple agreed entities or digital properties, with each assessment scope kept separate
Replace point-in-time manual compliance reviews with continuous, evidence-backed dark-pattern detection across your digital estate.