Category Archives: Cyber security

September 28, 2021 · 10:51 AM

Dark Data is an Unmet Cyber Security Challenge

By John Patzakis

Enterprises today are creating and storing massive volumes of unstructured, data distributed across the enterprise at a very fast pace. IT experts refer to this data type as “dark data.” Research advisory firm Gartner defines dark data as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.” according to Rahul Telang, professor of information systems at Carnegie Mellon University, “[o]ver 90% of the data in business is dark data.”

Dark data exists due to organizational silos and a highly distributed and mobile workforce, a trend that proliferated during the COVID pandemic and has now solidified as the new normal. As a result, there is a proliferation of unmanaged data stored in file shares, laptops, unarchived email accounts, shared cloud drives such as OneDrive and Dropbox and many other repositories. According to Anthony Juliano, CTO of Landmark Ventures, “dark data is exploding rapidly with the dissolution of the perimeter; it’s a largely unaddressed risk vector. A vast majority of the CIOs and CISOs I speak with are now prioritizing solving this problem not only going forward, but also backwards – and it’s not easy.”

Cyber security platforms generally have a good handle on perimeter integrity, encryption, and other key priorities such as zero day network attacks and malware. However, while these measures are clearly important, distributed dark data is largely a blind spot for cybersecurity tech, and as such organizations have very little visibility into the content of such data. GDPR, CCPA and other recent privacy regulatory requirements add increased urgency to this challenge.

CISOs and legal and compliance executives often aspire to implement information governance and security programs like defensible deletion, data migration, and data audits across their unstructured data to detect risks and remediate non-compliance. However, without an actual and scalable technology platform to effectuate these goals, those aspirations remain just that.

One tactic attempted by some CIOs to attempt to address this daunting challenge is to periodically migrate disparate data from around the global enterprise into a central location, such as an archiving platform. But boiling the ocean through data migration and centralization is extremely expensive, highly disruptive, and frankly unworkable for numerous reasons. While such a concept may seem like a good idea when drawn up on the whiteboard, originations quickly learn that you cannot just migrate hundreds of terabytes of distributed dark data to an archive, mainly due to network bandwidth and other logistical constraints, as well as the reality that you are merely copying and duplicating the data being migrated, which actually makes the situation worse.

Another tactic is data loss prevention (DLP). Again, this approach is thwarted by the new normal of a distributed, global workforce. Additionally, DLP tools are traditionally hampered by an inability to have deep content insight to unstructured data, resulting in false positives, inaccurate classification and unacceptable disruption to employee and business workflows.

What has always been needed is gaining immediate visibility into unstructured distributed data across the enterprise in-place, through the ability to search and report across several thousand endpoints, file shares and other unstructured data sources, and return results within minutes instead of days or weeks. None of the other approaches outlined above come close to meeting this requirement and in fact actually perpetuate information security and governance failures.

Born and bred to address global eDiscovery challenges, X1 Enterprise platform (X1E) represents a unique approach to dark data, by enabling enterprises to quickly and easily search across multiple distributed endpoints and data servers in place through a true distributed, parallelized computing architecture. Legal, security and compliance teams can easily perform unified complex searches across both unstructured content and metadata, obtaining statistical insight into the data in minutes, instead of days or weeks. With X1E, organizations can also automatically migrate, collect, or take other action on the data as a result of the search parameters. Built on our award-winning and patented X1 Search technology, X1E is the first product to offer true and massively scalable distributed searching that is executed in its entirety on the end-node computers for data audits across an organization. This game-changing capability vastly reduces costs while greatly mitigating risk and disruption to operations.

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Filed under CaCPA, Cyber security, eDiscovery & Compliance, GDPR, Information Governance, Information Management

June 23, 2020 · 9:36 AM

CCPA and GDPR UPDATE: Unstructured Enterprise Data in Scope of Compliance Requirements

An earlier version of this article appeared on Legaltech News

By John Patzakis

A core requirement of both the GDPR and the similar California Consumer Privacy Act (CCPA), which becomes enforceable on July 1, is the ability to demonstrate and prove that personal data is being protected. This requires information governance capabilities that allow companies to efficiently identify and remediate personal data of EU and California residents. For instance, the UK Information Commissioner’s Office (ICO) provides that “The GDPR places a high expectation on you to provide information in response to a SAR (Subject Access Request). Whilst it may be challenging, you should make extensive efforts to find and retrieve the requested information.”CCPA GDPR

However, recent Gartner research notes that approximately 80% of information stored by companies is “dark data” that is in the form of unstructured, distributed data that can pose significant legal and operational risks. With much of the global workforce now working remotely, this is of special concern and nearly all the company data maintained and utilized by remote employees is in the form of unstructured data. Unstructured enterprise data generally refers to searchable data such as emails, spreadsheets and documents on laptops, file servers, and social media.

The GDPR

An organization’s GDPR compliance efforts need to address any personal data contained within unstructured electronic data throughout the enterprise, as well as the structured data found in CRM, ERP and various centralized records management systems. Personal data is defined in the GDPR as: “any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.”

Under the GDPR, there is no distinction between structured versus unstructured electronic data in terms of the regulation’s scope. There is a separate guidance regarding “structured” paper records (more on that below). The key consideration is whether a data controller or processor has control over personal data, regardless of where it is located in the organization. Nonetheless, there is some confusion about the scope of the GDPR’s coverage across structured as well as unstructured electronic data systems.

The UK ICO is a key government regulator that interprets and enforces the GDPR, and has recently issued important draft guidance on the scope of GDPR data subject access rights, including as it relates to unstructured electronic information. Notably, the ICO notes that large data sets, including data analytics outputs and unstructured data volumes, “could make it more difficult for you to meet your obligations under the right of access. However, these are not classed as exemptions, and are not excuses for you to disregard those obligations.”

Additionally the ICO guidance advises that “emails stored on your computer are a form of electronic record to which the general principles (under the GDPR) apply.” In fact, the ICO notes that home computers and personal email accounts of employees are subject to GDPR if they contain personal data originating from the employers networks or processing activities. This is especially notable under the new normal of social distancing, where much of a company’s data (and associated personal information) is being stored on remote employee laptops.

The ICO also provides guidance on several related subjects that shed light on its stance regarding unstructured data:

Archived Data: According to the ICO, data stored in electronic archives is generally subject to the GDPR, noting that there is no “technology exemption” from the right of access. Enterprises “should have procedures in place to find and retrieve personal data that has been electronically archived or backed up.” Further, enterprises “should use the same effort to find information to respond to a SAR as you would to find archived or backed-up data for your own purposes.”

Deleted Data: The ICO’s view on deleted data is that it is generally within the scope of GDPR compliance, provided that there is no intent to, or a systematic ability to readily recover that data. The ICO says it “will not seek to take enforcement action against an organisation that has failed to use extreme measures to recreate previously ‘deleted’ personal data held in electronic form. We do not require organisations to use time and effort reconstituting information that they have deleted as part of their general records management.”

However, under this guidance organizations that invest in and deploy re-purposed computer forensic tools that feature automated un-delete capabilities may be held to a higher standard. Deploying such systems can reflect intent to as well as having the systematic technical ability to recover deleted data.

Paper Records: Paper records that are part of a “structured filing system” are subject to the GDPR. Specifically, if an enterprise holds “information about the requester in non-electronic form (e.g. in paper files or on microfiche records)” then such hard-copy records are considered personal data accessible via the right of access,” if such records are “held in a ‘filing system.” This segment of the guidance reflects that references to “unstructured data” in European parlance usually pertains to paper records. The ICO notes in separate guidance that “the manual processing of unstructured personal data, such as unfiled handwritten notes on paper” are outside the scope of GDPR.

GDPR Article 4 defines a “filing system” as meaning “any structured set of personal data which are accessible according to specific criteria, whether centralized, decentralized or dispersed on a functional or geographical basis.” The only form of “unstructured data” that would not be subject to GDPR would be unfiled paper records like handwritten notes or legacy microfiche.

The CCPA  

The California Attorney General (AG) released a second and presumably final round of draft regulations under the California Consumer Privacy Act (CCPA) that reflect how unstructured electronic data will be treated under the Act. The proposed rules outline how the California AG is interpreting and will be enforcing the CCPA. Under § 999.313(d)(2), data from archived or backup systems are—unlike the GDPR—exempt from the CCPA’s scope, unless those archives are restored and become active. Additional guidance from the Attorney General states: “Allowing businesses to delete the consumer’s personal information on archived or backup systems at the time that they are accessed or used balances the interests of consumers with the potentially burdensome costs of deleting information from backup systems that may never be utilized.”

What is very notable is that the only technical exception to the CCPA is unrestored archived and back-up data. Like the GDPR, there is no distinction between unstructured and structured electronic data. In the first round of public comments, an insurance industry lobbying group argued that unstructured data be exempted from the CCPA. As reflected by revised guidance, that suggestion was rejected by the California AG.

For the GDPR, the UK ICO correctly advises that enterprises “should ensure that your information management systems are well-designed and maintained, so you can efficiently locate and extract information requested by the data subjects whose personal data you process and redact third party data where it is deemed necessary.” This is why Forrester Research notes that “Data Discovery and Classification are the foundation for GDPR compliance.”

Establish and Enforce Data Privacy Policies

So to achieve GDPR and CCPA compliance, organizations must first ensure that explicit policies and procedures are in place for handling personal information. Once established, it is important to demonstrate to regulators that such policies and procedures are being followed and operationally enforced. A key first step is to establish a data map of where and how personal data is stored in the enterprise. This exercise is actually required under the GDPR Article 30 documentation provisions.

An operational data audit and discovery capability across unstructured data sources allows enterprises to efficiently map, identify, and remediate personal information in order to respond to regulators and data subject access requests from EU and California citizens. This capability must be able to search and report across several thousand endpoints and other unstructured data sources, and return results within minutes instead of weeks or months as is the case with traditional crawling tools. This includes laptops of employees working from home.

These processes and capabilities are not only required for data privacy compliance but are also needed for broader information governance and security requirements, anti-fraud compliance, and e-discovery.

Implementing these measures proactively, with routine and consistent enforcement using solutions such as X1 Distributed GRC, will go a long way to mitigate risk, respond efficiently to data subject access requests, and improve overall operational effectiveness through such overall information governance improvements.

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Filed under CaCPA, compliance, Corporations, Cyber security, Cybersecurity, Data Audit, GDPR, Information Governance, Information Management, Uncategorized

October 15, 2019 · 9:02 AM

Incident Reporting Requirements Under GDPR and CCPA Require Effective Incident Response

By John Patzakis

The European General Data Protection Regulation (GDPR) is now in effect, but many organizations have not fully implemented compliance programs. For many organizations, one of the top challenges is complying with the GDPR’s tight 72-hour data breach notification window. Under GDPR article 33, breach notification is mandatory where a data breach is likely to “result in a risk for the rights and freedoms of individuals.” This must be done within 72 hours of first having become aware of the breach.  Data processors will also be required to notify their customers, the controllers, “without undue delay” after first becoming aware of a data breach.GDPR-stamp

In order to comply, organizations must accelerate their incident response times to quickly detect and identify a breach within their networks, systems, or applications, and must also improve their overall privacy and security processes. Being able to follow the GDPR’s mandate for data breach reporting is equally important as being able to act quickly when the breach hits. Proper incident response planning and practice are essential for any privacy and security team, but the GDPR’s harsh penalties amplify the need to be prepared.

It is important, however, to note that the GDPR does not mandate reporting for every network security breach. It only requires reporting for breaches impacting the “personal data” of EU subjects. And Article 33 specifically notes that reporting is not required where “the personal data breach is unlikely to result in a risk to the rights and freedoms of natural persons.”

The California Consumer Privacy Act contains similar provisions. Notification is only required if a California resident’s data is actually compromised.

So after a network breach is identified, determining whether the personal data of an EU or California citizen was actually compromised is critical not only to comply where a breach actually occurred, but also limit unnecessary or over reporting where an effective response analysis can rule out an actual personal data breach.

These breaches are perpetrated by outside hackers, as well as insiders. An insider is any individual who has authorized access to corporate networks, systems or data.  This may include employees, contractors, or others with permission to access an organizations’ systems. With the increased volume of data and the increased sophistication and determination of attackers looking to exploit unwitting insiders or recruit malicious insiders, businesses are more susceptible to insider threats than ever before.

Much of the evidence of the scope of computer security incidents and whether subject personal data was actually compromised are not found in firewall logs and typically cannot be flagged or blocked by intrusion detection or intrusion prevention systems. Instead, much of that information is found in the emails and locally stored documents of end users spread throughout the enterprise on file servers and laptops. To detect, identify and effectively report on data breaches, organizations need to be able to search across this data in an effective and scalable manner. Additionally, proactive search efforts can identify potential security violations such as misplaced sensitive IP, or personal customer data or even password “cheat sheets” stored in local documents.

To date, organizations have employed limited technical approaches to try and identify unstructured distributed data stored across the enterprise, enduring many struggles. For instance, forensic software agent-based crawling methods are commonly attempted but cause repeated high computer resource utilization for each search initiated and network bandwidth limitations are being pushed to the limits rendering this approach ineffective, and preventing any compliance within tight reporting deadlines. So being able to search and audit across at least several hundred distributed end points in a repeatable and expedient fashion is effectively impossible under this approach.

What has always been needed is gaining immediate visibility into unstructured distributed data across the enterprise, through the ability to search and report across several thousand endpoints and other unstructured data sources, and return results within minutes instead of days or weeks. None of the traditional approaches come close to meeting this requirement. This requirement, however, can be met by the latest innovations in enterprise eDiscovery software.

X1 Distributed GRC  represents a unique approach, by enabling enterprises to quickly and easily search across multiple distributed endpoints from a central location.  Legal, cybersecurity, and compliance teams can easily perform unified complex searches across both unstructured content and metadata, and obtain statistical insight into the data in minutes, instead of days or weeks. With X1 Distributed GRC, organizations can proactively or reactively search for confidential data leakage and also keyword signatures of personal data breach attacks, such as customized spear phishing attacks. X1 is the first product to offer true and massively scalable distributed searching that is executed in its entirety on the end-node computers for data audits across an organization. This game-changing capability vastly reduces costs and quickens response times while greatly mitigating risk and disruption to operations.

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Filed under compliance, Corporations, Cyber security, Cybersecurity, Data Audit, GDPR, Information Governance