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.
Text IQ’s clients rely on our platform to execute precision responses to some of the largest data breaches in recent history and to avoid scenarios like the Equifax data breach settlement. At the core of our platform is the ability to quickly and accurately create an entity-centric report detailing whose data has been breached, and which types of sensitive data have been compromised for each individual. This enables a smooth and cohesive internal investigation, and largely eliminates the need for manual effort in the breach notification process.
Search terms and regular expression (RegEx) rules have become outdated and have failed to keep up with the nuances and challenges of today’s messy data. Text IQ’s AI platform identifies PI data in a much more sophisticated manner. Our algorithms reduce false positives and false negatives by understanding the context of each word in a document, enabling it to identify sensitive information even when not explicitly labeled or misspelled.
Automatically link PI to a discoverable catalog of PI data types.
Eliminate redundancies and inconsistencies when consolidating entities in your data.
Accurately detect concepts that capture special category information, like political beliefs.
Apply these with the click of a button and receive complete, original files with these actions applied.
Easily review and train machine learning models using our intuitive annotation interface to improve accuracy, and reiterate this learning across datasets.