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.

GDPR Compliance Questions Answered

Q&A with Nick Brandreth, VP of Sales at Integris Software

This interview first appeared in the GDPR Report.

Nick Brandreth leverages over 16 years’ experience in the Information Security sector, having worked at firms including Safebreach, cyber-security firm Imperva, and Tripwire, where he was an early proponent of DevSecOps through the work of Gene Kim.

The GDPR report caught up with Nick at the Data Protection World Forum in London to find out more about how companies’ data privacy strategies need to adapt to modern demands and data structures.

What role do you think automation plays in the future of data privacy?

Nick Brandreth: Automation is fundamental to data privacy, especially when it comes to all the risks that come with bringing new data into your organisation.

Automation is even more crucial given how data creation has exploded over the last few years. Our handheld devices are constantly creating data, our tablets are continuously streaming, and our locations are always being tracked through our devices. The capacity for data storage has become very cost-effective, so it’s relatively cheap for organisations to store all their data indefinitely.

Companies collect this data and utilise it in innovative ways that help drive technological advances and offer better products and services to consumers, which in turn, increases revenue for the organisations themselves. However, with increasingly severe regulations, like GDPR and now CCPA, companies now need to view their data as both an asset and a liability.

In this new climate of regulation, understanding what data you have and where it resides has become increasingly difficult. Traditionally, organisations have dealt with this via a manual, survey-based process, which is prone to human error. With data constantly changing due to acquisitions, data sharing agreements, or marketing departments purchasing data, these manual based approaches are insufficient, and if anything, expose the company to more risk by creating a false sense of security about where and what data they actually have. Given, all of the innovation that has transpired with the way data is now collected and used, innovation is needed to understand what the data means to the company which is why an automated approach is now so crucial.

How have previous data privacy strategies been generally inadequate?

NB: Inadequate may not be the correct term for it, I think antiquated might be a better way to put it. It helps to think about this in terms of Gartner’s three Vs of big data: volume, velocity and variety. Any type of solution must handle these three Vs. A manual survey-based approach or trying to use tools not build with big data constructs can’t address the three V’s.

As an example, previously, data might have just been held in structural databases around which organisations wrapped tight controls. It was much easier for them to identify where sensitive data might be, how its being used and if data handling obligations such as retention, residency, etc. are being followed.

Today, the situation is very different. Data now constantly moves through an organisation, and customers and companies are constantly sharing data back and forth. Further, the definition of what is sensitive data has evolved where data such as diet preferences or personal days taken off can infer religion, and movies watched can infer behaviour.  Additionally, exact definitions of what is personal will only be born out in case law as time goes on, making compliance a moving target.

While privacy regulations have been around for a while, GDPR has given privacy real teeth and pushed the need for more organisations to find ways to have a comprehensive, defensible data privacy strategy.

What does it mean for automation technology to be scalable and flexible?

NB: We can point back to Gartner’s three Vs of big data: volume, velocity and variety. For data automation to be scalable and flexible, it needs to handle data at any scale. We’re not just talking about gigabytes or terabytes – in fact, we’re starting to talk about petabytes, exabytes and zettabytes. In essence, data privacy automation technology needs to be able to handle an extremely large volume of data at large scales from various internal and external sources.

Now this where we start the conversation around inter-flexibility. For instance, I have structured data, but I also have a lot of unstructured data sitting out in various sources. I may have a data lake and data streaming in and out of my organisation. I may use Workday, which holds my HR data, and then I may use Salesforce for my sales/marketing data. The bottom line? I need to be able to handle all those types of data.

In short, any type of automation needs to be able to look at scale and flexibility.

What do you predict for the US in terms of a national GDPR-style regulation?

NB: This is a very interesting question and one that is currently getting an increasing amount of attention in the U.S. The current administration does not favour regulations, so it’s hard to say whether there will be a national GPDR-style regulation. However, many tech giants have gotten ahead of the conversation and are already talking with policy people about what a national privacy framework would look like. These talks at some point will surely involve consumer advocacy rights, as you see leaders of large tech firms commenting that privacy and transparency is a right. Additionally, States have started to take the reins by driving their own privacy regulations. California gets the top headline as it’s a pretty stringent state, but many states have now passed regulations or have them in legislation. Many of these states are actually focused on ensuring that your data is de-identified as required.

When regulation is at a state-by-state level rather than at a federal level, regulatory complexity becomes exponential for organisations. This points back to the importance for data privacy automation technology to be scalable and flexible – the ability to scale to different rules and mandates and map that many-to-many relationship between the data and the obligations. The complexity of the data privacy challenge is constantly rising and won’t slow down anytime soon. Organisations need to be aware of this and future-proof any Data Privacy or Data Protection program.

How awake to the importance of data privacy are consumers in the US?

Consumers in the US are much more aware of data privacy regulations now; GDPR really opened a lot of eyes when companies became frenzied to comply in advance of the deadline.

Now that technology has enabled so much personalized consumer data to be discovered and aggregated, consumers are starting to wake up to the fact that data breaches are extremely difficult to prevent, and their focus has turned to the need for data privacy and to demand for more transparency from businesses on the issue.

What are the differences between data security and data privacy?

NB: Having spent so many years in Information Security, data privacy and security are really part of the same continuum. However, data security concerns “how” data is secure, and data privacy thinks about “what data and why?”

For a company to truly secure their data, they must know and understand what exact data they have in the first place. Once they have this they can then ensure security policies are being followed. For example, is all data that should be encrypted actually encrypted; or is sensitive data that should be located in only certain sources actually only in those sources or has it proliferated to other sources in the environment. Without automation, these questions can’t be accurately or sustainably answered.

Data Privacy Automation provides extra security within an organisation because ensures many of the data security policies you put in place are being followed. There is an asymmetrical war is being fought against companies, and while organisations can’t afford to fail, the attacker only needs to succeed once. Not having an empirical idea of exactly what data you have and here is yet another increase in risk to the organization.

Data Privacy Automation gives security teams the ability to come in and be very precise with securing the right data in the right way so that organisations can continue to innovate and serve their customers by using their most asset.

 

The team at Integris Software proudly supported the Hopper X1 Seattle Conference, supporting women pursuing careers and success in technology.  The Hopper X1 Seattle Conference is organized by AnitaB.org and modeled after the Grace Hopper Celebration which is the world’s largest gathering of women in technology.

One of our core people tenants at Integris is that we celebrate and continually foster our diverse and inclusive culture. It was an opportunity not only to learn from many great technologists, but also to make many meaningful connections with other women in engineering who are stepping up to not only close the gap we have for skilled workers in tech, but also the gender gap we have historically had in tech. Throughout the event, there were many stories of strength and testaments of overcoming adversity to achieve powerful successes, and inspiration was had by all. And wow – did we have a great time!

As a female-founded company, we are excited to be included in the AnitaB community and will continue to support ongoing efforts in Seattle to close gender gaps. And, to that end, we want to give one last shout out – to all the men and gender-neutral individuals who also chose to attend HopperX1. It was a poignant reminder that inclusion works both – or rather, all – ways.

Meredith Turner, our Head of People Experience led our participation in the event along with Software Engineer Elizabeth Williams.

More information is available on the Seattle AnitaB.org community from a series of fantastic blogs published here.

 

A Values-Driven Commitment to Data Privacy

The values of Integris Software are the building blocks of who we are and how we operate. Transparency is vitally important to us, and we want to make it known that the values we believe in most inform our everyday decisions.

Integris wasn’t created to just solve data privacy concerns, but rather, to work toward something much more important: transform and foster the data privacy environment we believe the world wants – and most importantly, deserves.

Our world is rapidly evolving, and technology advancements continue to drive the exponential growth of data. Increasing demands for personal, on-demand experiences have required large amounts of personal information to be stored and used. The speed of change has created difficulty with how to regulate, collect and use data.

Companies in charge of our data must operate with unwavering integrity

Not taking data privacy seriously is gambling with the wellbeing of people’s lives and organizational success versus failure. A lack of data privacy has real-world consequences: drained bank accounts, damaged credit, and even stolen identities.

Making data privacy a priority sets the precedent that it is critically important. The business world follows trends, and organizations that live their truths can make lasting impacts on the world around us.

At our core, Integris is an organization backed by the highest commitment to integrity. It lives in our name and is central to our operations, partners and solutions – day in and day out. We respect each other and the collective power that is achieved when the team goes all in.

There isn’t always consensus on what is right and wrong. Around the world, people value privacy, and more importantly, history shows us the consequences when privacy is compromised.

Privacy is a right, not a privilege

Integris was founded on the belief that the right to data privacy is absolute. A lack of privacy isn’t just an inconvenience; it’s a possible nightmare for consumers and businesses alike. Privacy keeps our intellectual property and personal information just that — private.

Laptops, smartphones, watches, televisions and even kitchen appliances capture data constantly in our everyday lives. This has normalized data collection, with consumers trusting that their data is being handled discreetly. Unfortunately, that is often not true due to the enormity and complexity of data, and the manual nature of most data privacy programs these days.

Together, we must bring our collective commitment to the values we share and embody to the forefront.

Integris will never back down from doing what is right. We will always stand with those willing to go out on a limb, trust their instinct and challenge the status quo. We have a vision of what we believe the world can be when you focus on doing what is right as the rule, not the exception.

Privacy is a continuum

The future is coming fast and will require data privacy solutions that are flexible, continuous and devoted to excellence. Simply being reactive isn’t enough anymore. A data privacy solution that works today may not work tomorrow. We must keep innovating – pushing the boundaries, uncovering the unknown and stepping up to take smart, calculated risks.

Setbacks are inevitable, but perseverance, resourcefulness and a commitment to excellence ultimately prevail. Sometimes it requires taking the road less traveled to get where you want to go. At Integris, we believe there is a better, clearer path to data privacy, and our solutions will get organizations to that outcome. The future of data privacy should make consumers feel safe, knowing their privacy is protected.

A lifetime commitment

I founded Integris Software in 2016 to create a product and culture we take great pride in – as a company, a team and as global citizens. Leveraging our values has brought us to a new phase in the data privacy arena and drives what we do going forward. We keep our values close and apply the knowledge we gather every day to create a safer today and a more private tomorrow.

GDPR is here. Are you ready?

Regulations like GDPR and the California privacy law are triggering knee-jerk reactions as companies lock down their data for fear of misuse. This overreaction is how most organizations protect personal information.