The digital trade-off —the exchange of privacy in return for free services—has broken down. Consumer expectations have changed, and regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are pitting business models against new privacy rights: the right to know what information is being stored, the right to access it, and the right to control it. Your existing data processes are breaking under the pressure, and your business is left to hang in the balance.
Focus on privacy,
or focus on the business.
Text IQ for Privacy achieves data privacy compliance with one strategy to reduce risk, time, and cost. Confidently retrieve any instance of personal information from your structured and unstructured data, paired with meaningful business context. Then, incorporate insights back into The AI Brain for human-driven analysis, and thoughtful action at scale.
Improve recall by up to 45%. Reduce the risk of exposing missed PI, along with reducing the risk associated with human error.
Review up to 80% fewer documents. Spend less time and manual effort to validate that all PI has been identified.
Scale your privacy processes and reduce the number of false hits for reviewers to consider.
Text IQ’s Artificial Intelligence and Machine Learning platform is used by leading enterprises and data privacy teams for a number of key use cases detailed below
Under new legislation such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), Data Subject Access Requests (DSAR) have become major compliance burdens for enterprises. In order to uphold these privacy ethics, leading enterprises are challenged to synthesize numerous sources of structured and unstructured data in order to compile an individual’s personal information to comply with these new regulations. Text IQ’s platform is able to accurately and efficiently amass a requester's information from scattered data sources and fulfill their request under GDPR compliance.
Text IQ’s Machine Learning algorithm accurately detects the entities and personal information (PI) in each document, and understands the connection between unstructured PI and the relevant entity. This linkage ability allows Text IQ’s platform to automatically redact the correct PI when a document has multiple entities involved.
Creating a report of each relevant document in a Data Subject Access Request (DSAR) request is currently an extremely onerous task. Text IQ makes creating a document-centric report easy by automatically detailing which documents contain what types of PI, involve which entities, and other pertinent details.
With an increasingly complex regulatory landscape, determining whose data has been impacted is critical to an effective, timely and accurate data breach response plan. However, the data assessment phase for breach response plans is often time consuming, resource intensive and error-prone.
Not only do manual and inaccurate breach assessments put compliance timelines and brand trust at risk, current approaches fall short of identifying personal information and sensitive data incorporated in emerging data breach regulations.
Text IQ’s Data Breach IQ tackles the data assessment bottleneck, enabling teams to make quicker, more informed, and more accurate decisions on who to notify based on applicable regulations and relevant attributes.
Text IQ automates the generation of an entity-centric report for each individual impacted, linking personal information and combinations of data discovered through context-aware AI.
Text IQ’s clients rely on our platform for greater decision-making clarity at scale when it counts.
In contrast to approaches relying on search terms and keywords, Text IQ incorporates semantic analysis, human signals, and context to improve accuracy for PII like Social Security numbers as well as identify sensitive data such as political opinions, genetic data, and race and ethnicity that existing approaches would miss.
Text IQ takes an entity-centric approach to map, link, and associate all relevant data elements to individuals, automatically determining residency by individual. Data breach response teams can easily determine which regulations apply to which individuals and whether the impacted data attributes meet the threshold for notification.
Minimize manual effort & time for assessment with AI detectors.
Accurately detect sensitive data elements using semantic analysis and relationship mapping.
Find and index data associated with an individual through AI, Entity Linking and Normalization.
Streamline notifications by merging multiple entries for a single individual.
Enhance accuracy over time with continuous learning as human input is integrated into AI model.