Sensitive or confidential information is a byproduct of doing business for many companies. From internal and external communication via email, chat, and other channels to various databases, document repositories, and legacy systems—the sheer volume of data, and number of sources it comes from, is dizzying. Once the various data types, including images, video, and audio, are thrown into the mix the challenge of information management becomes even more complex.
An unfortunate consequence of this information overload is that private, confidential, or otherwise sensitive information can be left unsecured or accidentally disclosed. With the number of data breaches this year already surpassing last year’s total, data privacy and security has never been a more pressing issue. However, it isn’t just data breaches that pose a threat. According to a survey conducted by Consilio, 64% of legal technology professionals feel that inadvertent disclosure of sensitive data is the biggest data security risk companies face today.
Regulations aimed at data privacy have become more commonplace and increasingly strict due to mounting concern around how information is gathered, stored, used, and shared at scale. The General Data Protection Regulation (GDPR), an EU legal framework that went into effect in 2018, is widely considered the toughest data privacy law in the world. Although the U.S. has not yet adopted such stringent regulation at the federal level, things are shifting.
The California Consumer Privacy Act (CCPA), a state-level regulation that went into effect in July of 2020, was modeled after GDPR and has set a precedent for data privacy regulations in America. This trend is global, with Gartner estimating that “by 2022, half of the planet’s population will have its personal information covered under local privacy regulations in line with the General Data Protection Regulation (GDPR), up from one-tenth today.”
Not only is proactively safeguarding data the best way to avoid becoming the subject of an unwanted headline, it’s increasingly mandatory for compliance with global data privacy laws. For companies with large, complex sets of data simply identifying where sensitive information is stored can be daunting. Fortunately, artificial intelligence (AI) makes it possible to quickly locate and automatically redact sensitive information from virtually any data type.
Automatically obfuscating information, by blacking it out, deleting it, or other means, is called automatic redaction or auto-redact. Although automated redaction is typically associated with text, such as removing social security numbers from a store of documents, AI has made it possible to auto-redact sensitive information from virtually any data type. This includes unstructured data, such as video, images, and audio.
Redact tools from super.AI leverage artificial intelligence to apply automated redaction to images and video. For example, personal identifiable information (PII) such as faces, license plate numbers, and addresses can be automatically blurred from images and video footage. These tools are designed to reliably anonymize images, videos, documents, and other unstructured data at scale—meaning thousands of images can be redacted in seconds. Users are able to upload data, define the objects that require redaction, then let Super.AI do the rest.
Unstructured data is an increasingly important component of information management. According to research from AIIM International, 26% of information management professionals feel that integrating new data types into governance programs is the biggest information management challenge for their organization. As unstructured data grows to become the largest data type businesses store, the risk of unintentionally disclosing confidential or sensitive information increases.
Automated redaction makes it possible to ensure sensitive information remains protected as the types and quantity of company data grow. It also minimizes human error in governance programs by allowing artificial intelligence to automatically redact sensitive information across any data type by default. This allows enterprises across most industries to more efficiently and effectively mitigate compliance risks. Automated redaction has wide (and likely unforeseen) applicability, with some specific examples including:
If you think automated redaction sounds like it might benefit your business, or you simply want to learn more about the technology, check out the following resources: