Cyber Resiliency Summit | Mark Ward, Congruity360

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Mark Ward

COO

Congruity360

Three insights you may have missed from theCUBE’s Cyber Resiliency Summit

Cyber resilience is no longer just an IT concern. It’s become a strategic imperative for businesses facing an onslaught of artificial intelligence-driven threats and sophisticated ransomware attacks. Organizations are adopting proactive, integrated approaches that blend technology, people and processes to safeguard operations and ensure business continuity in the face of evolving threats.This shift demands collaboration across business functions, rigorous testing and strategic planning, according to industry analyst Jon Oltsik, during theCUBE’s recent Cyber Resiliency Summit

From data chaos to control: A new era dawns in cyber resiliency

In a digital age teeming with vulnerabilities, managing unstructured data has become a linchpin for cyber resiliency. Unfortunately, this critical security frontier is often overlooked in traditional data security strategies.Congruity360 InfoGov Inc. is reversing that trend by leveraging data classification to shine a light on unseen and hidden data assets, according to Mark Ward (pictured), chief operating officer of Congruity360.Congruity360’s Mark Ward talks about data security posture management with theCUBE

play_circle_outline Enhancing Compliance through DSPM, Data Risk Management, and Data Classification for Valuable Insights

play_circle_outline Impact of data protection decisions on cost savings and risk reduction

play_circle_outline Enhancing Scalability and Performance through False Positive Reduction and Legacy System Integration

play_circle_outline Effective Data Security Strategies: Remediation, Optimization, and Safe Storage Solutions

play_circle_outline The Future of AI in Data Analysis, Decision-Making, and Customer Inquiries: Leveraging Risk Detection Models

play_circle_outline AI Wave's Impact on Data Security and Economic Efficiency with Microsoft's Copilot and Purview

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Mark Ward, Congruity360

Mark Ward

COO Congruity360

At the Cyber Resiliency Summit, Mark Ward, COO of Congruity360, joins theCUBE Research's Christophe Bertrand to explore the company's innovative approach to unstructured data management. Ward details Congruity360's focus on data classification to identify and mitigate security risks, offering assessments that help clients understand and enhance their security posture.

Find out more about theCUBE's coverage of the Cyber Resiliency Summit https://www.thecube.net/events/thecube/cyber-resiliency-summit

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What is data security posture management and how does it help with compliance and cyber resiliency? add

What was the impact on the budget for the large European customer's Cohesity new environment after reducing the amount of data by 60%? add

What approach does the speaker believe is more effective for scalability in security posture management compared to legacy providers in the field? add

What are some options for handling end data that has been classified, and how does the safe storage solution differ from traditional deletion methods? add

What capabilities of AI are being leveraged currently and what future developments can be expected in terms of data analysis and decision-making support? add

What company has done a great job introducing AI technology, specifically with Copilot and Purview, to show the importance of classifying data? add

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Mark Ward, Congruity360

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Christophe Bertrand

>> Welcome back to the Cyber Resiliency Summit from theCUBE. My name is Christophe Bertrand, Principal Analyst here at theCUBEResearch. I am joined today by Mark Ward, who is the Chief Operating Officer of Congruity360. Mark, welcome.

Mark Ward

>> Thanks, Christophe. Great to see you.

Christophe Bertrand

>> So Mark, Congruity360, that's a great company name. Tell us more about what you do.

Mark Ward

>> Congruity360 is the end result of a journey by its founders over about 20 years. So the Congruity in the 360 is really about taking all the values that have been acquired from servicing both the data center and the major data apps to a company that is focused specifically on unstructured data management. And we'll get into, I'm sure, the details of what we view that as. But at kind of a 30,000-foot level perspective, we're leveraging the promise and the capabilities of data classification. So I'll kind of dig into that, I'm sure, over the period of this discussion.

Christophe Bertrand

>> Great. And a lot is about data security posture management, right? So I'd like to talk about that with you, talk about what you're doing in the context of cyber resiliency, and also talk about what you're doing to help with compliance because these are, I think, very intertwined and part of your DNA. So let's talk about data security posture management, DSPM. I think it's an interesting segment or market that seems to start to combine with other cyber security segments and storage and backup and recovery and compliance. Can you help us understand this set of capabilities better and where it fits and what it does?

Mark Ward

>> Yeah, I think it all starts with the enterprise recognizing, Christophe, that they've spent an enormous amount of time and resource on protecting what's inside the corporation. And now what they've learned and what they're spending time on is what actually are those assets, those data assets that they own? So for us, data security posture management is all about unraveling that that isn't seen or is hidden. So what we focus on is identifying in the first step all of the attributes associated with your unstructured data that could potentially give you or can give you very real actionable data to take remediative action on. So for example, are there files that are existing in open shares with open read rights? Is there data in that environment that has PII or sensitive or risk data? Is there data that hasn't been accessed in over 10 years? Has PII data existed in an open share by an employee that left five years ago? So it's really that understanding of what you currently have and giving you very tangible items to act on, integrating those items with a actual remediation workflow and automating the cleansing and the overall data security posture of your environment.

Christophe Bertrand

>> Right. So let's unpack this a little bit because I think there are really three big areas that sort of stand out. I was hinting at them earlier. You talked about PII and compliance. I think that's pretty straightforward. This is clearly an area of potential risk and cost. The risk of noncompliance is well understood and pick your poison on this one, and that's a big issue. The other that I see here is obviously linked to the cyber resiliency conversation and really looking at that combination of data and the threat and the exposure of that data becoming either available for encryption or available for leakage or for other monetization by the bad guys, so that proactive threat mitigation. And then, I think really having this understanding of risk overall of your data assets. And you used the term data asset, which I think is extremely accurate here. So let me ask you, it's not a trick question, but look, I don't think I've ever met an end user, a customer when I was on a vendor side that ever told me, "Well, I really have a hundred percent visibility over all of my data." So how do you reconcile that? What do you do to help?

Mark Ward

>> Yeah. So we offer an assessment that is very easy to consume, whether in the cloud for cloud data or on-prem, all sorts of varieties of SaaS platforms as well. The Dropbox is the box of the world. You have your PowerScale from Dell, you have your NetApp from NetApp, you have your Asabi, you Cloudian, all of these sources as well as SharePoint online, Azure, and AWS. What we're able to do is give you a snapshot of your current state of security posture management in a week. And what that allows the customer to do is, number one, focus on their most risk-adverse applications, focus on the business entities that they care the most about. We come in and do this very lightweight scan of their unstructured data, and yield for them based upon their priorities or characteristics of that that they care about, "Here are the most compelling data points for you to take a remediated action on." Those things are very frequently a migration to a lower cost platform. They can include air gap to remove it. So from a network access perspective, we're actually taking that data and moving it to an air-gapped or a net-gapped repository. It can include an approach to change the permissions for the access controls, so that data is locked down to a certain extent and we're protecting, making sure the wrong people don't have access to it. In many cases, we're actually tagging the data, Christophe. And this comes up, and I'll use an example of one of our largest Fortune 100 customers, an enormous semiconductor equipment manufacturer. Their number one concern is internal insider threat management as well as outsider. So they're able to tag documents with hidden tags so that the user community doesn't know that they're being tracked, and appropriately then track the access controls associated with that. We're seeing this more and more often from our large customers, as they care not just about the third-party guys on the outside, but they care about, and I'm not saying malicious behavior, but maybe just naive or not educated behavior by some of their users putting data that is very important at risk.

Christophe Bertrand

>> Right. And we know that one of the biggest risks, and we heard it from one of our partners here and participant at the summit, is definitely social engineering. So it is reasonably easy and a pretty high reward if you can get in the system through someone who's not paying attention is being scammed or conned into doing something they shouldn't be doing. But what I'm hearing is very interesting, and I want our viewers to really sort of take a step back here and listen, think for a second about what you just said, which is literally what you do in one week. So one week from now, you could have a full map of all of your data with a good understanding of the risks, of the good things and the bad things, all the risks, the access control issues, the wrong location, also the economics of it, having some data that hasn't been touched in a while in the wrong tier of storage. Sounds like we've been talking about this for years, but the truth is it keeps happening, and it keeps happening against the backdrop of data being created massively more and more frequently with volumes that never stop to grow. So it's a never-ending issue. Now, I had a quick question on this. So this is a one-time thing. You get a baseline, but shouldn't the end users and the companies that you work with actually do this on a regular basis anyway?

Mark Ward

>> Yeah, absolutely. Let me be crystal clear. That assessment is targeted at one of their data sources, not 10 petabytes of data. So we work with the client, we work with the client's business customer, and we say, using your social engineering example, "Let's focus on HR data." We will then go and get all those shares and folders associated with the HR environment, whether some exist in the cloud or some are on-prem, and we'll do that analysis. To your point, as soon as our clients and our partners, folks like Deloitte, see what is in that information, see the economic or what I refer to as the business impact associated with that data and how it can be more efficiently and securely managed, really the wallets open up and the awareness opens up that there is actually very simple steps that are available to be taken to solve for these big both cost points, for example, Christophe, we all know that tier one NAS systems, whether you're a NetApp or a PowerScale customer, this is expensive or vast even now, a very hot commodity in the marketplace. These are expensive systems. If you're storing data that is seven years old and never been touched in seven years on these very expensive systems versus moving it to a tier of object either on-prem or in the cloud, these are very expensive decisions to make. And couple that with being able to clean up your risk profile at the same time, the risk element gets paid for by the cost mitigation side of the equation.

Christophe Bertrand

>> I think that's a very powerful message and a great one to hear, because there's one thing I know for sure. Budgets tend not to be very sensible these days, so it's never happened to me anyway.

Mark Ward

>> Christophe, if I could, and just going back because I'm so familiar with your industry knowledge in let's talk about data protection. As you know, data protection is an industry that is growing dramatically and changing. And thus, many of our customers come to us, and they do a security posture analysis prior to making that decision to move from an IBM solution or a Veritas solution to a Cohesity or a Rubrik. And what we're able to show them is that, for example, 40% of their environment is consumed by snapshots that they've been collecting over three years. There's been no cleaning up, there's been no modifying of it. Likewise, they have 61 copies of the same file that they've been backing up over and over again. So for one large customer in Europe, we were able to reduce the amount of data that they were going to have to budget for their Cohesity new environment by 60%. That was almost $600,000 in savings by recognizing that they had, I'll call it copy explosion in their existing system and nobody had gone through to clean it up.

Christophe Bertrand

>> Well, that's very interesting and I think that the net net on this is that there's also the whole security dimension with it. So you get the economics, you get to understand your risk profile. You get to fix the issues so that you can be more cyber resilient. It sounds like the perfect solution. But let me push back a little bit on this.

Mark Ward

>> Sure.

Christophe Bertrand

>> What about integration with legacy systems? There's lots of data and there are lots of systems that have been around for a long time. How do you integrate with this? How do you deal with, there is a lot around DSPM, again data security posture management, a lot around false positives. Do you think there's a problem? There's really no problem, so now you end up spending time for nothing? So how do you deal with that? And then, you suggested scale and scalability, so clearly that's another component. So let's talk about these three topics: scalability performance, false positives, and then legacy environments.

Mark Ward

>> Yeah. So on the scalability side of the house, we really think we've got this one nailed. Many of the pre-existing legacy providers in the, I'll call it security posture management space, have taken an approach where not only do they want to do a file or a metadata attribute level review of the data, but they actually want to go in and open up all the content. And this goes to your false positive question. They really want to make sure that there is not a false positive that could affect their overall security posture. We believe it's very important to do that content level ingestion, but it's step two. Step one, and if I could use the needle in the haystack type approach for you, is those false positives are those needles. In order to get to those false positives the fastest, we do a metadata or a file attribute scan that allows us to focus in and eliminate redundancy. So doing a hash value compare and eliminating all the redundant data in many cases can take anywhere from 30 to 50% of the entire storage environment out of that content, I'll call it inspection process. Anytime we're able to eliminate data from the content inspection is a benefit for the customer, because the industry kind of governed by some laws of physics, right? If you have to go in and open up a file, there is some laws of physics applied to it, and you don't want to have to do that more than you have to. So our first step is, "Clean up on aisle three," so get rid of all the stuff that you really don't care about. Start focusing on the data, because in many cases in the actual file name, you'll be able to identify if it has risk data in it. Focus there first and get the job done in a fraction of the time. The second scalable is the segue to your question on performance. So if I was to do this like a lot of legacy customers, I'd go back to the nature of web crawling. I'm sure you're familiar with it, how the search companies would go and create their corpus of data for a search. That takes a long time and is again very expensive. We don't take that approach. We use an API integration approach. So we've actually been benchmarked in scanning a Dell PowerScale system 15 times faster than any of the pre-legacy solutions that the customer has used, both from Dell and from third-party vendors. So for us, it's all about performance and scale combined together, because we have clients that have literally 100 petabytes of data that they're trying to find both that needle in a haystack, at the same time reducing their risk exposure, I guess is the right word. And then, that final comment that you asked about on that false positive, it is the content level comparison that we will do that'll actually determine whether that data is a risk element that fits against the policy models that you've put into our system. All that's done automatically. But again, this concept of crawl, walk, run is an approach that we've brought to companies like Bosch, companies like Blue Cross Blue Shield, companies like Coinbase that have taken literally petabytes of data that they had no visibility to and remediated it down to both reduce risk and reduce cost.

Christophe Bertrand

>> Quite a few nice logos here. I'm sure there are plenty more. Well, okay, so I think this answers a lot of the questions that I had around performance and also scalability, definitely an interesting two-tiered approach here. So that's really the observation part, right?

Mark Ward

>> Mm-hmm.

Christophe Bertrand

>> That's assessment observation, and that's what in a sense, security posture management does. Maybe that's where it stops. Is that enough? What do you do about remediating the problems?

Mark Ward

>> Yeah. And thank you for recognizing that it's not enough or asking the question. My answer is it's not enough. From the identification process and really where we've been able to differentiate ourselves to the Fortune 100 and to the SMB as a different way to catch mice, I use the joke, "We're not a different mousetrap, we're a different way to catch mice." So eliminating all of the efforts associated with a legacy content-generated approach, focus on a file scan approach first to find the needle in the haystack. Classify the data through our classification models, and use the power of machine learning to go drive that review in an automated process. The end data that gets classified then gets handed off to a number of different remediation options. Everyone falls into the immediate thought, "Oh, deletion." Very rarely, Christophe, are large corporations going to kind of bless a deletion event. But pretty much all of our customers and our prospects are looking to reduce costs by moving data that isn't frequently accessed. We talk about redundant, obsolete, and trivial. Move that data to a much less expensive storage medium and protect it from malfeasance in a different way. The last part of the equation, and this is a new offering that a couple of our customers just in Q4 started to consume from us, is what we call our safe storage solution. This is a solution that actually provides a full audited ability to move the data and restore the data to its original spot. And that data could be stored in the customer's environment. That data could be stored in the cloud, or it can be stored in a new safe storage environment that we have in one of our data centers. So the restoration is kind of the last part of the value proposition. You start with identification, you move to classification, you remediate, you restore, and then you have our full audit capability, so chain of custody throughout all of those steps.

Christophe Bertrand

>> So I think we've covered a lot of ground here. We've talked about the infrastructure use cases, how you optimize, you just mentioned that, which is great. And by the way, optimization economically, but also from a risk standpoint, you're moving essentially data that could be exposed out of the way.

Mark Ward

>> Reducing surface attacked area, for sure.

Christophe Bertrand

>> Essentially reducing the surface attack, exactly. We talked about the compliance piece. Obviously, that's great. There's a new thing called AI, I don't know if you've heard about it. So obviously, you talked about you using ML, which I think is really the right term, and I'm sure there's also some AI you will leverage from a feature standpoint. So I'd like to get your view, and I'm going to ask you to do it in sort of two sides of that same in imperial coin, if you will. So how do you leverage ML and AI in your solution? What are your plans, high level? And I think you've already hinted at some of that. And secondly, what about those AI workloads and AI processes, for lack of a better term, that also generate a lot of data, that leverage a lot of data, are you doing anything about those?

Mark Ward

>> Yes. So great. Yeah, the world really is driving a lot of, I'll call it solution architectures to compliance, and AI is one. So to answer your first question, what are we doing today? So today, we're leveraging a very sophisticated machine language learning system, and the models that we train our data against are the, I'll call it the regulations primarily that exist throughout the world. So one that we do quite a bit of business with some of the world's largest insurance companies is NYDFS. So we take the NYDFS characteristics, the data elements that the financial services industry requires their customers to protect, again, mostly sensitive and personal information so that Bank of America doesn't let Christophe and Mark's home address, Social Security card number become available in either the dark web or by a nefarious attack. So NYDFS is one of the regulatory compliance agendas that is driving customers. We are constantly updating our models to incorporate any of the new regulatory requirements that get added to NYDFS. And NYDFS is just one of many. Another one, I mentioned, Blue Cross Blue Shield. So people talk about HIPAA regulations, but really what the industry brackets that underneath is what we call high trust. So high trust has all of the HIPAA as well as many other different client, I'll call it information or file data repositories that need to be protected from being made available to other folks. So machine language learning models trained by our regulatory corpuses of data so that we can automatically find those elements in people's data that could potentially put them at risk. The second attribute of leveraging AI now is we're moving from, I shared with you our assessment. So our assessment today is delivered to a client in our dashboards. It's a web application. It can also be exported to CSV and we can provide you a Word document. In the near future, we're going to have an AI model, or think of it as a wizard, that'll allow the customer to ask questions of our system. And the system itself will then provide the data outputs that are relevant to any of their questions. So those questions could be around data obsolescence. Those questions could be around data security posture management, specifically for the HR application or the engineering application, only relevant to SharePoint documents, not relevant to CIFS shares in their PowerScale system. So being able to leverage the capabilities of AI to be that data analyst or the data scientist that the customer will use to help them make better decisions is something that you can look forward to us delivering in the market in the very near future. And then, the last part of your ask was overarchingly, will this whole AI wave, I've got to give Microsoft credit. And very rarely have I seen a company do as great a job introducing such a massive wave of technology as they have. But what they've done with Copilot and using Purview as a kind of front end to show people why they need to classify data. Now, mind you, it's only Microsoft data, so they're not doing what we do. They're doing a very small portion of it. But they've trained the world to understand that you need to classify data. You need to identify data before you classify it, and then you need to hand it off to a smart system. And that's exactly what we're doing for the Fortune 1000 and the SMB marketplaces. We're giving them ability through our SaaS data delivery to consume the values of data security posture management, the values of AI. AI specifically, it's all about taking risk out of the data before you feed it to your ChatGPT engine or whatnot, or your own private system. But it's not just that, Christophe. Why would you hand an AI engine 50% of the data that's a copy of the data? You want to take that out because as you know, that vast system's pretty expensive, and those Dell GPUs are pretty expensive. If you can feed it a third of the data that it's currently being fed right now, you're going to have an enormous economic impact.

Christophe Bertrand

>> Well, Mark, thank you very much for covering this use case. I'm very interested in how much more you do around supporting these AI workloads and clearly economic operational risk benefits. I think definitely something our viewers should be thinking about as they improve their security posture, improve their resiliency, optimize their costs. It was great to have you on this segment, so Mark, thank you so much for joining us today.

Mark Ward

>> It's a pleasure. Thanks so much, and thank you for providing this Silicon angle.

Christophe Bertrand

>> And to our viewers, thank you so much for watching this segment. Please stay tuned for more in our Great Cyber Resiliency Summit. My name is Christophe Bertrand, Principal Analyst here at theCUBEResearch. Stay tuned.