Do you conduct interstate business in the US? Prepare now for CCPA.

The California Consumer Privacy Act was passed in June 2018 and goes into effect in January 2020. Although it’s ostensibly a state law, CCPA is trying to forge a de facto standard for data privacy in the US in the absence of federal legislation. CCPA is similar to GDPR in that it uses economic presence to urge other regions – US states – to adopt similarly high standards. But GDPR and CCPA do have their own requirements and nuances, and a compliance program specifically architected to address GDPR will not necessarily translate. Businesses with interstate operations will need to take a more holistic and less regulation-specific approach to data management and compliance to remain competitively viable.

The analyst firm 451 Research published the report The California Consumer Privacy Act: not just ‘America’s GDPR’ in March 2019. Integris Software is pleased to offer complimentary access to the report to help companies understand and prepare for the requirements of CCPA. Here are a few highlights of the report:

  • Data privacy and data protection around the world has reached a tipping point. The EU’s GDPR, in effect since May 2018, has been a model for other countries concerned about consumer privacy protections. Moreover, individuals are becoming more aware and more educated regarding the value and sensitivity of their data. 
  • How companies handle individuals’ personal data affects consumer trust and confidence in those companies. A recent 451 Research survey shows that 26% of US consumers are less trusting of US businesses than they were one year ago. Significantly, 90% of the survey respondents expressed concern about the ability of the companies they do business with to adequately protect their personal data. 
  • Most large businesses in the US have California residents as customers, thus pressing the adoption of CCPA’s standards elsewhere in the nation. Other states are in the process of developing their own privacy laws. What could result, in the absence of a federal standard, is disparate privacy requirements in the US, with each state having different protections for its residents. 
  • GDPR and CCPA have much in common in their core principles, but they also differ significantly in the details. It’s key for organizations is to tackle core, shared requirements at the architectural data management level and address individual nuances of each regulation with tools higher in the stack only as necessary. Such an approach allows for flexibility amid evolving regulations, and ultimately, cost savings. 
  • Data privacy and data protection regulations are largely more process-oriented than they are technology-oriented. Investment in platforms that help coordinate processes across various data protection and data privacy stakeholders can especially benefit the business, even when these platforms do not exert direct control on data themselves.

Learn how GDPR and CCPA are similar as well as how they differ. Read the full 451 Research report here.

Nine industry experts provide sage advice on how to protect your customers’ sensitive data in 2019, as well as some things you should NEVER do

C-suite execs have a lot on their plates when it comes to protecting their customers’ sensitive data in 2019. Please tell us:

1)  What’s one sage piece of data privacy management advice or tip to help them in 2019?

2)  What’s one thing they should never do, or a pitfall to try to avoid in 2019?


David Hoffman

Associate General Counsel and Global Privacy Officer, Intel, Inc.

@hofftechpolicy

1)  As we move towards a more data-centric economy, understanding what data an organization has, and how it is be used, is critical for both shareholder value and protecting privacy. Organizations need to build the right processes to map where their data is, how they can make innovative use of it, and how they will show they are accountable to the individuals to whom the data pertains.

2)  An organization should never rely solely upon third parties to have access to data without showing they will be accountable for how it is used. Upstream and downstream data inventory management will be critical in 2019.


Cameron Etezadi

Deputy CTO, SAP Concur

@cetezadi

1) Modern enterprises move data through streaming pipelines, where it can be hard to protect provenance and canonicity. Tracing where the data ends up is as essential as protecting the data itself. Once you publish data internally, it can be very hard to control where it goes or how it’s used. Enterprises should implement strong controls and good hygiene in establishing trust and access – and then verify the results.

2) Avoid playing “catch up”; many organizations end up “compliant” at a point in time but gradually fall apart as new software is written or deployed, new people join the organization, etc. Protection is a process that many organizations fail to bake into the upfront architecture of their projects and then scramble towards when it’s too late. It’s too easy to leave holes, pay too much, or find yourself in an impossible situation if privacy is always a bandage applied at the end.


Jennifer Leggio

Chief Marketing Officer & VP Operations, Flashpoint

@mediaphyter

1)  Engage in external collaboration and information sharing. There are a number of secure, trusted communities that facilitate these activities among security and privacy practitioners, so if your company isn’t already a member, join one. These communities range from large and industry-specific, such as the various ISACs, to small and vendor-specific, but all exist for the same reasons: to provide like-minded experts with the means to quickly and easily share relevant information with, and seek guidance from, other like-minded experts. Doing so can expose your company to greater resources and expertise that can help you to better protect your customers’ data.

2)  Never conflate compliance with security. GDPR, for example, has fueled great progress in how companies address the privacy of customer data, but the standards it enforces are by no means sufficient for securing customer data. This is largely because there are many critical areas of security that GDPR does not regulate, including encryption, security awareness and education, business continuity and penetration testing, and technical and policy controls, to name a few. The same goes for similar compliance bodies such as PCI DSS and HIPAA. Just because a company is deemed compliant does not mean that its customers’ data is fully immune to compromise. Compliant businesses can and do experience data breaches, which is why achieving compliance should be never be viewed as an end goal—but rather as one of many essential components of a comprehensive security strategy.


Kristina Bergman

Founder, CEO at Integris Software

@KristinaKerr

1)  Know where your data is. When I was working in venture capital five years ago and first started researching the data privacy space for investment purposes, I found that the biggest glaring problem was that no one knew what data was where, let alone if they were in compliance with any laws, contracts, or other business obligations. The foundation to complying with any law, whether it’s GDPR or CCPA or any of the other new bills being considered, is to know what data you have.

2)  Never assume that your work is done just because you’ve got great policies and procedures in place. A key component to ensuring compliance, or at least defensibility, is the operationalization of those policies and procedures. Being able to audit your data sources to prove compliance with the law is critical to protecting your brand and reputation.


Marc Groman

former Senior Advisor for Privacy in the Obama White House
former Chief Privacy Officer of the Federal Trade Commission
Principal, Groman Consulting Group LLC
Adjunct Professor, Georgetown University Law Center

@MarcGroman

1)  Today, data often is a company’s most strategic and valuable asset. Companies must treat it that way, by implementing a comprehensive, enterprise-wide, continuous and risk-based privacy and security program. Step one – know what data you have.

2)  Never make assumptions about the data your organization collects, creates and stores. Rely on facts, evidence, and documentation.


Barbara Cosgrove

Chief Privacy Officer, Workday

@cosgrove_barb

1)  Privacy will be center stage in 2019, so be proactive and reevaluate your processes to ensure that you not only remain GDPR compliant but also anticipate any future U.S. and global privacy legislation that could be coming in 2019. Start by establishing a cross-functional taskforce to perform an assessment of your current state of compliance. During this process, take the time to understand what’s been successful and where there have been challenges from a business perspective prior to introducing new processes. Once you’ve done that, map new laws and regulations to your existing controls and processes, and determine where you may be required to implement changes. Once implemented, be sure to set a regular cadence for the taskforce to regroup to assess compliance.

2)  With new regulations like the California Consumer Privacy Act (CCPA) in the pipeline, be sure you don’t evaluate them in isolation. Build a comprehensive privacy governance framework, which enables you to continually re-assess your compliance with existing privacy regulation like GDPR and emerging ones.


Mark Kraynak

Entrepreneur, Venture Partner, Aspect Ventures

@AspectVC

1)  Get a handle on employee/contractor off-boarding. Latent privileges are a big, unnecessary risk. Also, automate your process of understanding what sensitive data you have. Most organizations have too much data in too many places for human processes to be reliable or consistent enough to be effective.

2)  Don’t stop at Encryption. The most common pitfall I see is when I hear someone’s answer about data security is, “We encrypted our data, so we don’t need to do anything else.” Encryption is good for mostly bulk, mostly static use cases, but tends to fail for data in use.


Paige Bartley

Senior Analyst – Data, AI & Analytics at 451 Research

PaigeBartley
@451Research

1)  View data protection and data privacy as an opportunity, rather than a burden. There’s a pervasive enterprise perception that consumer controls for data will result in less analysis and insight, or that privacy controls somehow “lock down” data. This misses the bigger picture. Data-driven regulation, such as GDPR and similar mandates, all share the same common requirement of strong, granular control of data at the architectural level. Strong control of data, in turn, has downstream benefits for other proactive data-driven initiatives within the organization. A robust data protection and privacy program, implemented enterprise-wide, has benefits for data quality, coordination of self-service access rights, and building consumer trust. At a high level, data privacy and data protection requirements are a golden opportunity to reconsider and optimize data management architecture and practices.

2) We’ve officially entered the data protection and privacy era, and the enterprise can no longer have a combative attitude towards compliance if it wishes to remain competitively viable. The biggest pitfall is viewing data privacy or data protection requirements as a list of burdensome technical “checkboxes” that need to be ticked off one by one for each new regulation. This view of the individual trees misses the broader forest: the core principles that are shared across regulatory frameworks. Implementing new siloed tools and new processes for each new regulation is not sustainable, economical, or scalable. Instead, organizations need to focus on optimizing underlying data management architecture and workflows from the ground up. Focus on the core commonalities, rather than the differences, between regulations. From there, implement highly-specialized point solutions higher in the stack only when necessary.


Craig Speizle

Managing Director, Agelight Digital Trust Advisory Group
Founder & Chairman Emeritus, Online Trust Alliance

@craigspi
@onlineintegrity

1)  2018 will likely go down as the year of questionable ethics. From the data sharing and mining practices of Facebook, Google and most recently the Weather Channel’s app, to the abuse of social networks, we all need to be concerned. All too often these entities who were supposedly “stewards of our privacy and trust” appear to have acted unethically. While executives need to be held accountable, one has to also question employees who failed to come forward and follow their own moral compasses. Our industry is at the center of a seismic change with the convergence of big data and artificial intelligence (AI). The oceans of digital information and low-cost computing power are providing endless marketing opportunities. At the same time, we are being confronted with ethical dilemmas challenging users’ digital dignity and redefining privacy norms.

My big focus for 2019 is Data Ethics: the convergence of big data, AI and Ethics. I have been steadfast on the need to move from compliance to stewardship. Ethics is an extension to this in light of the practices and what I call “data laundering,” which is occurring.

The question at hand is: Can industry and governments be trusted to responsibly regulate AI? Second, how can ethical guardrails be developed to help prevent abuse? There is no question AI will have a profound effect on how marketers engage consumers. Done right, consumers will get better and more relevant ads, content and services. This can be a win-win, but only if we get it right and address the ethical unintended consequences in advance.

2)  The one thing executives should avoid is remaining silent. They need to question their business and data strategy and not fall silent like so many employees at offending companies have. The question is, are they willing to follow their moral compass and rise above compliance?

CCPA has frequently been compared with the EU’s GDPR. While the regulations are similar in ethos, they have fundamental differences that reflect subtly divergent cultural attitudes and approaches toward data privacy and consumer rights.

 

Feel like data privacy and protection requirements are a chore? Turn them into a business advantage.

Most organizations view data privacy and data protection regulation such as GDPR and CCPA as a costly and time-consuming burden. However, the core data management capability required for compliance – granular data control – is also necessary for proactive leveraging of data for business purposes. Companies should view data control and architectural optimization as a strategic opportunity to uncover hidden insight and benefit their data-driven business initiatives.

The analyst firm 451 Research published the report Architectural data control: turning privacy requirements into a blessing, not a curse in January 2019. Integris Software is excited to make the report available help companies see the dual-sided benefits they can derive from holistic data control. Here are a few highlights of the report:

Both regulatory compliance and effective leverage of data share the common requirement of granular data control. Today’s data privacy and data protection requirements, then, should be viewed by the enterprise as an opportunity to optimize data management architecture from the ground up.

  • Organizations cannot protect or provide privacy controls for data if they cannot quickly and consistently locate data, identify and resolve duplicates, accurately associate personal information with identities, and enforce policies. Both structured and unstructured data must be controlled with the same rigor.
  • With strong data control capabilities, the effects of silos are minimized, resulting in the ability to aggregate and analyze diverse data sources in a more contextual way. Data privacy and data protection mandates effectively shift the balance of power back from data quantity to data quality.
  • When consumers or data subjects are given more choices over the use of their data, trust is fostered. When a trusting relationship is built, consumers voluntarily provide more accurate information over time. Consumer trust, in turn, is correlated with more profitable lifetime relationships, lower churn, and more positive word-of-mouth presence in the market.

In summary, data control is the common requirement for both reactive compliance and proactive data leverage capabilities. It is also essential to building trust with consumers that drive long-term profitability. If the enterprise is to strategically fulfill compliance requirements while maintaining the ability to competitively maximize the insight it derives from data, it must optimize its data management architecture and strive toward a unified view of data.

Learn more about the myriad benefits of architectural data control in this complimentary 451 Research report here.

On-demand webinar on how to prioritize privacy while driving data innovation

This webinar features Integris Software Founder and CEO Kristina Bergman.

Key topics covered include:

  • Data protection challenges amid growing regulation and public mistrust
  • Understanding the data privacy and data security continuum
  • The benefits of moving from manual surveys to data privacy automation
  • Data privacy automation case studies
  • Integris product overview

Both regulatory compliance and effective leverage of data share the common requirement of granular data control, which needs to be addressed at the architectural level.

Integris enables accurate, continuous defensibility to meet California Consumer Privacy Act compliance requirements

5 Ways to Ensure Your Data Storage Systems Protect Customer Data

This article first appeared in TheNewStack.

Five-hundred million. That’s how many individuals recently found themselves getting a notice that their personal information had been compromised in the recent Marriott data leak. The seemingly endless disclosure of major breaches (another 100 million from Quora was announced as I started writing this article) are causing an awakening among consumers and regulators.

While Marriott’s database had been hacked and malicious actors had unfettered access to its data, many companies struggle to maintain control of the private data that their employees, partners and customers entrust them with. The sad fact is that customers no longer trust organizations to protect their data and therefore are very concerned about the type and volume of private data that organizations hold. It’s not enough to claim security best practices. Customers want to know what and why companies have their private data.

Data protection is the responsibility of all of the technical teams at a company. But data storage administration and configuration are crucial in ensuring that the private data is protected, whether it be customer PII or your research team’s IP. Here are five tips to help ensure that data is handled responsibly.

1)  Know What, Where, and How Much Private and Sensitive Data Is Held by the Organization

This is often easier said than done. Traditional solutions like Data Loss Prevention have promised to find and classify our data, but scalability issues, the inability to identify data in motion and lack of accuracy continue to plague DLP offerings.

Modern technologies such as Docker containers and Kubernetes clusters running in auto-scaling cloud platforms such as AWS, Azure and GCP, can eliminate scalability issues. We often find the largest volume and highest rate of data collection to be in big data lakes. It can be very useful to make use of the compute power built into such data lakes in the form of map reduce jobs to scan, label and classify data at scale.

Data knows no boundaries. Private and sensitive data can be anywhere. Efficiency means having visibility into your data — whether it’s structured or unstructured, in a traditional DBMS or big data lake, out in the cloud or in your data center.

So, while you’re in the process of discovering data, it’s not enough to look only where it should be. You must have the capability to search, discover and classify data everywhere it resides.

2)  Map the Data Journey

Data is often the currency of business today, which means that data is constantly moving throughout the customer or product journey. Data is either a byproduct of customer activity or is actively requested and collected. Data is bought and sold to other organizations. And as a result of this data in motion, private data can be exposed in channels that aren’t designed to hold it.

While I wouldn’t ever put my private details such as account number or password into the chat box that seems to pop up on every website offering to help, my mother does this all the time. While providing such a service is important and valuable to the business, monitoring data traveling through these channels is critical to ensuring that private and sensitive data is kept in the proper location and scrubbed from areas such as chat logs.

It’s imperative to identify all the places that data moves in or out of your systems. Watching data as it moves across all touch points can provide verification that data is flowing in compliance with regulations, policy, contracts, or other obligation. Monitoring data in motion can help you stay ahead of any problems.

3)  Check to See That the Data That Should Be Encrypted Actually Is

Encryption is certainly not a panacea for all sensitive data issues. But at the same time, encryption can be a powerful mitigating control — but only if the data that should be encrypted is encrypted. If all social security numbers (SSNs) in a table are meant to be encrypted, are they actually? You have to check to make sure.

This should start with checking the accuracy of the initial discovery and classification effort. If it’s just assumed that all SSNs in a table are in the correct column and that column is encrypted, can you be sure all SSN’s are encrypted? Data often finds its way into unexpected places. This leads not just to problems of encryption, but also mis-classification and mis-categorization. And this can be the data most vulnerable, as it’s often not watched as closely, leading to the next point.

4)  Don’t Stop with Users

Sensitive data is not always attached to users, so, don’t limit your search to user-based data. Also, consider derived sensitive data as seemingly innocent data points can lead to very private information.

Organizations generally focus on user accounts and the data associated to those accounts. But as discussed earlier, data is often misplaced. Is an SSN any less sensitive because it’s not linked in the database to a first and last name? Of course not. On the other hand, seemingly non-sensitive data can become sensitive when it is linked to a user.

For example, it’s unlikely you have religion listed with employee names in your HR database. But you probably do have requested days off. It’s often easy to derive a religious preference from the PTO days an employee requests. And while this data might not be used or even understood by the employer, it will certainly be understood and used by a third party who might have access to this seemingly innocent data. Sensitive data is sensitive data and should be treated as such.

5)  Know Your Data Obligations

Private and sensitive data comes with obligations from regulations, external requirements and internal policies. How do you know if you’re meeting all of these?

You’re most likely familiar with obligations in the form of internal policies. These policy obligations might be regarding which data elements should be encrypted, what data should be backed up, and the service level agreements on the restoration of such data.

And you might also be familiar with regulatory obligations like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPPA), Sarbanes Oxley (SOX), Payment Card Industry Data Security Standard (PCI-DSS) and others.

However, obligations can also be contractual. Are you buying, selling, or otherwise transacting data with other third parties or partners? There are typically contracts in effect that place obligations on that data. Obligations can also be public statements, such as a privacy statement made on the company’s website. A data privacy strategy should include visibility into such obligations and evidence that they are being met. Knowing the relationship of data to the obligations on that data can certainly make life easier when questions arise.

Conclusion

Building a system that protects private data is crucial. Whether you’re spinning up a new development environment for a new venture or simply conducting an audit to ensure compliance with the shifting regulations and privacy laws, how you structure your data storage and management technologies can have a significant impact on your company’s success. Making sure you’re protecting all of your data from various sources at all times is essential — and failing to do so can be costly.

The current regulatory environment is driving urgency to meet modern enterprise data handling challenges

At their core, data privacy regulations like GDPR and the California Consumer Privacy Act (CCPA) require good data handling practices. Continuous defensibility to meet compliance requirements boils down to doing two things well:

1)  Understanding where sensitive data resides across all data source types.

This should include structured, unstructured, semi-structured, data in motion, at rest, on-premise or in the cloud. The ability to scale up and down is critical.

 

2)  Mapping data back to existing data handling obligations.

Not just regulations, but also contracts and internal policies, as well as the ability to take action within your data ecosystem, such as encrypting files, or processing a consumer’s data access request.

Seven data handling best practices

Having visibility into where sensitive data resides and tying it back to obligations is critical to enabling these seven data handling best practices:

1)  Implement data security controls.

Documenting policies are important, but to be defensible you need to be able to show that you can identify different types of sensitive data across your enterprise, and that you have compensating controls in place to keep it encrypted, hashed, or masked. Be cautious about solutions that simply map IDs to pre-existing metadata. You’ll run the risk of creating a false sense of security about the data you have, which security parameters are being applied, and whether they’re in compliance with regulatory mandates. Metadata can be misleading. Integris operates at the data element level to inform you exactly what personal information is in your dataset, not just what the metadata implies. By using a combination of contextual awareness, natural language processing, and machine learning, Integris maps all sensitive data elements so as to assess privacy, integrity, and handling violations.

2)  Establish and enforce a data retention policy.

You probably have different retention policies for different types of data. Make sure you’re calculating retention in a consistent way such as creation date, date of last transaction or another metric. Of course, to be defensible, you’ll need to be able to identify your sensitive data, and show that you’re adhering to your own retention policy.

3)  Identify mislabeled data.

Data handling policies only work if your data has the right labels. For example, it’s not uncommon to find databases backing webforms to have mislabeled data. For instance, a customer accidentally typing in their credit card number in a phone number field could put you in violation of a regulation, because you’re not encrypting the phone number column in your database.

4)  Identify misclassified data.

Much like mislabeled data, misclassified data poses a significant risk. For example, SSN’s found in a phone number column will not have a high enough classification tied to the data set. Don’t rely on manual data mapping efforts, which can be riddled with errors. Integris automates the identification of misclassified and mislabeled data, then surfaces issues for human intervention or kicks off automated remediations.

5)  Tackle data proliferation, including data in motion.

You probably have data handling policies that restrict where sensitive data resides. For example, it must sit in Oracle or Hadoop, but not in network file storage or Dropbox. For data streaming into an organization from places like Facebook, Instagram, or business partners, data in motion can be a big blind spot. Identify and monitor your data streams to ensure you know what is entering and leaving your organization and that you are adhering to all data handling policies. Integris’ ability to handle data in motion is key to helping you understand which data is entering or leaving your organization via data sharing agreements, and the streams and feeds your company relies on for continuous innovation.

6)  Residency-based policy-making.

Both GDPR and the California Consumer Privacy Act (CCPA) indicate that data handling policies apply differently depending on a person’s residency or citizenship. Track data against residency policies to ensure effectiveness. Integris can infer residency from geospatial data, a country code, or phone number.

7)  Handle what GDPR calls data subject access requests (DSAR).

Under both GDPR and CCPA, individuals have the right to inquire about their personal data, what data companies collect about them, how it’s being used or shared, and to exercise their right to “be forgotten.” In order to address DSAR, you must understand where all personal data resides and be able to map it back to your users.