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At Seyfarth, I’m not just an attorney—I’m also an ethical hacker and digital forensic expert, and I’m proud to be one of several “attorneys who code” at Seyfarth. Here, we’re passionate about technology, and we routinely seek creative and innovative ways to leverage technology to enhance client services.

I’ve found that one area where emerging technology can make an enormous impact is in the data breach notification assessment space. Specifically, I’ve found that artificial intelligence can power the evaluation of implicated data for PII to determine notice requirements in the various implied jurisdictions. While there are many ways to accomplish that evaluation, I wanted to share my experience partnering with Text IQ, a company that builds AI for sensitive information, to power a data breach response in a blind study alongside the traditional document review and coding approach. The result was reduced risk, quicker turnaround time, and cost reduction for Seyfarth’s client.

Casting an Epidemic

The specter of a data breach is an unfortunate reality for anyone that uses a computer. Corporations are obviously large targets, with potentially thousands of employees doing things on computers. Some of those things are good, and some put the company at risk. Aside from the rapid evolution of attack sophistication and complexity, regulators are also raising the stakes in terms of liability for data breaches and security incidents

Privacy-related data incidents and data breach matters are governed by a proliferating list of statutes: GDPR, CCPA, the New York SHIELD Act, and many others from various states and companies. After they experience a security incident and confirm a data breach, corporations tend to rely on traditional methods to evaluate their exposure and act accordingly.  However, these methods are breaking down in the face of burdensome regulatory reporting requirements, often within highly constrained timeframes. One of the shortest of these is GDPR’s “long weekend” reporting period of only 72 hours.

As companies grow, their potential attack surfaces expand accordingly. This is evident in data breach statistics. One data breach tracker estimates that 68 records are stolen every second, thanks of a broad cast of bad actors:

  • State-Sponsored attackers attempt to steal intellectual property in order to create cheaper products. These entities are also manipulating enterprise offerings for their own benefit, as we’ve seen with the nefarious use of Facebook advertising out of Russia, for example. 
     
  • Hacktivists want to “punish” a company under some perceived ethical duty. As Greg Palm, the former General Counsel of Goldman Sachs, said at #TheInevitable 2019, the gravest consequences of reputational events like these play out not in the court of law, but in the court of public opinion.

  • Economic hackers try to steal banking information and re-route assets to themselves. These hackers can cause turmoil large enough to affect stock prices, which they leverage to trade equities or derivatives. 
     
  • Thrill hackers, to put it simply, mess with companies because they can.
     
  • Internal threats come from disgruntled or careless employees who create a leak, or departing employees who take intellectual property to feather their nest at their new competitive employer.

An Assessment Playbook

In the wake of a security incident, a decent incident response will generally take some form of the following course:

  • Investigate what data may have been taken;
  • Determine what data actually left the organization or was exposed to an unauthorized party;
  • Assess the corpus of information that has been compromised or potentially compromised;
  • Determine what information has been compromised for specific individuals, based on the broadest sense that covers all possible jurisdictional and regulatory requirements of an organization’s likely document populus;
  • If necessary, report the assessments to the appropriate authorities in the appropriate jurisdictions;
  • Provide notice to the individuals with particularity about what data was exposed;
  • Potentially provide credit monitoring;
  • And potentially prepare for litigation or regulatory action based on the size of the incident.

Out with the Old

Relying on traditional methods, this assessment can be a significant challenge for identifying personal information (PI), which includes both personally identifiable information (PII) and protected health information (PHI).

In the status quo, search terms and search expressions may be used to find patterns and PI, and contract attorneys are hired to review the documents and log PI that has been compromised to support the various notice requirements of any jurisdiction that’s implicated.

This traditional model has inherent barriers:
 

  • Understanding the individuals whose PI has been exposed in a dataset is a challenge that calls for an entity view. But the status quo provides only a document view with embedded entities.

  • There are myriad types of documents that could contain PI. How does one account for all potential government IDs, tax documents, bank documents, licenses, etc.?  Even with complex and comprehensive RegEx, there’s a risk of missing “models” of PI that may exist across the world. As a result, search terms and expressions suffer from inconsistent results and may miss non-obvious data. Unstructured data sources are very difficult to submit to this kind of process.

  • Similar to the above, the concept of “a search” as a function, with its roots in techniques like Boolean Search and document retrieval, was never designed to navigate large-scale unstructured data, like the data that is exposed in a breach.
     
  • Search terms yield results that are both over- and under-inclusive, requiring extensive human review. And humans are inefficient and error-prone at poring over large amounts of data. We have inconsistent decision-making across brains, and we also tend to provide typos and other ephemera that introduce incorrect data into PI assessment logs.

Taking the above obstacles into account, Seyfarth’s cybersecurity attorneys have begun leveraging artificial intelligence in more of our processes, including data breach PI assessment. Our Fortune 200 clients in particular have experienced for themselves how using AI can automate rote and low value work, like document review, and augment high value work—lending human subject matter expertise to exercise judgment and give legal advice.

In our first project with Text IQ, we leveraged its AI-powered solution, Text IQ for Legal, to reduce the cost and burden of conducting privilege reviews and generating logs. Subsequently, we used another of its offerings, Text IQ for Privacy, in a Proof of Concept project to identify PI after a client suffered a data breach. To compare Text IQ with traditional document review, we conducted a blind study of human versus machine.

The results speak for themselves:
 

  • Reducing risk: Text IQ achieved a recall rate (the amount of PI it successfully retrieved) of 98%. This was a 61% increase in recall compared to the traditional method.
     
  • Reducing time: Text IQ eliminated 93% of the documents from needing human review, while search terms eliminated just 45%. This was an 87% decrease in volume.
     
  • Reducing cost: Because Text IQ could confidently rule out so many more documents, it saved over 1,000 contract attorney hours, or an estimated $80,000 in reduced document review and project management time.

AI for Data Breach Response

Being an (ethical) hacker of things and naturally curious technologist, I wanted to know more about how it works. There are three innovations that have allowed Text IQ to achieve this kind of accuracy in PI detection.
 

  • Social Linguistic Hypergraph™: Text IQ combines social signals with language signals to do something better than perhaps any other AI company out there: find all traces of an individual in a dataset. Its trained machine learning models can understand meaning on a semantic level (e.g. what meaning is intended), as opposed to merely a lexical level (e.g. what terminology is used). As a result, its AI can detect concepts that capture special category information, like political opinions, genetic data, and race and ethnicity.
     
  • Continuous Learning: Text IQ generates interactive dashboards with automatic PII and PHI linking, powering drill-down analysis and data exploration. The user can override or select highlighted PI in each document, and that feedback is automatically re-ingested into the machine in an iterative process that allows the models to self-improve over time.
     
  • The Human Index™: In addition to document-centric reports, Text IQ provides entity-centric reports with individuals in a column, and all their associated PI traces in rows. This is a new view that allows for a question that we couldn’t ask before: what are all the traces of PII and PHI that exist in this dataset for this individual?

Conclusion

Relying on the status quo to understand large-scale unstructured data is risky. It’s also potentially time-consuming and expensive. Today, AI can completely and reliably automate the low value work of PI identification in document review and reduce risk. It lets cybersecurity practitioners like me provide more accurate, less costly, and less risk-laden results to clients.

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The information in any resource collected in this virtual library should not be construed as legal advice or legal opinion on specific facts and should not be considered representative of the views of its authors, its sponsors, and/or ACC. These resources are not intended as a definitive statement on the subject addressed. Rather, they are intended to serve as a tool providing practical advice and references for the busy in-house practitioner and other readers.
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