Cyber Sentries: AI Insight to Cloud Security

SIEM Speed Without the Sprawl—DataBahn’s Take on Security Data Pipelines
In this Cyber Sentries: AI Insights for Cloud Security episode, host John Richards sits down with Dina Kamal, Chief Revenue Officer at DataBahn, to tackle a familiar cloud security problem: teams can’t get the right data into the SIEM fast enough, and when they do, costs and noise spike. After the introductions, John and Dina dig into why data integration and parsing often consume most of the timeline in SIEM projects—and how a security data pipeline layer can compress onboarding from months to weeks.
They also explore what “doing more with less” looks like in a modern SOC: filtering and routing data based on detection value, preserving what’s needed for compliance, and keeping flexibility for SIEM migrations. Dina’s bigger point is that AI only becomes truly useful when it’s paired with domain expertise and real operational context—otherwise it’s easy to end up with impressive-looking outputs that don’t hold up under investigation pressure.
Questions We Answer in This Episode
  • Why do SIEM projects stall on data onboarding, and what speeds it up?
  • How can you cut SIEM ingestion costs without weakening detections?
  • What does owning your security data change during SIEM migrations?
  • Where does AI help most in SOC workflows, and where do guardrails matter?
Key Takeaways
  • Data pipelines remove SIEM “plumbing” bottlenecks by automating collection, parsing, and transformation.
  • Cost reduction works best when you filter by security value, not just by volume.
  • Decoupling data collection from the SIEM reduces lock-in and simplifies vendor changes.
  • AI is strongest when guided by security context and experienced practitioners.
The throughline is practical: better detections and faster investigations start upstream with intentional data handling. By treating the SIEM as a high-value analytics destination instead of a dumping ground, teams can regain capacity, reduce noise, and keep options open as tools and vendors change. And when AI is applied to the right parts of the workflow—with clear constraints and real-world context—it can accelerate outcomes without compromising trust.
Links & Notes
  • (00:04) - Welcome to Cyber Sentries
  • (01:02) - Meet Dina Kamal
  • (03:14) - Data Pipeline Management
  • (05:55) - The Target
  • (07:32) - Changing Vendors
  • (08:34) - No Storage
  • (09:31) - Why People Need It
  • (13:09) - Ahead of the Curve
  • (19:54) - Capturing the Data
  • (23:02) - Useful Data
  • (26:02) - More with Less
  • (27:03) - Visibility
  • (29:40) - When to Start
  • (31:04) - Wrap Up

Creators and Guests

Host
John Richards II
Head of Developer Relations @ Paladin Cloud The avatar of non sequiturs. Passions: WordPress 🧑‍💻, cats 🐈‍⬛, food 🍱, boardgames ♟, a Jewish rabbi ✝️.

What is Cyber Sentries: AI Insight to Cloud Security?

Cyber Sentries explores the critical convergence of AI, cloud, and cybersecurity, diving deep into how these three pillars are actively redefining the modern Security Operations Center (SOC). As the threat landscape grows in complexity, we showcase the accelerating role of AI in defending cloud infrastructure, applications, and data. Join us as we illuminate this high-stakes intersection—a space where cutting-edge innovation meets the necessity for continuous vigilance—to transform how organizations approach resilience in a digital-first world.

John Richards:
Welcome to Cyber Centuries from Cyberproof on TrueStory FM. I'm your host, John Richards. Here, we explore the transformative potential of AI for cloud security. This episode is brought to you by Cyberproof, a leading managed security services provider. Learn more at cyberproof.com. On this episode, I'm joined by field CTO at DataBahn, Dina Kamal. We talk through how data pipelines, AI, and security all come together, changing how teams think about security, visibility, and what data they actually need to keep. We also unpack why AI on its own isn't enough, and how combining domain expertise, real world context, and AI is where real value shows up. Hello, everyone. Welcome. We are joined today by Dina Kamal, field CTO at DataBahn. Dina, how are you doing today?

Dina Kamal:
I'm doing fantastic. Very happy to be with you here today, John. Very exciting.

John Richards:
Well, I'm so excited to have you here on the show today, and we're going to be talking about something that's kind of new and emerging in the space, and so I'm excited to hear from you on that, but before we dive into a little more about the space that DataBahn is in, I'd love to hear about your journey to being CTO there, so can you talk through how you got to this spot? What's it look like to end up being a field CTO there?

Dina Kamal:
No, of course. I'll give you the cliff notes, as we say in Canada. So, I was a senior partner in Deloitte, a cybersecurity partner, built a lot of security operation centers on my day all over the world. Then, I moved to the AI practice, became an AI partner. Nanda Santhana, the CEO of DataBahn, who I worked with briefly in another life, if you will, reached out to me to get my feedback on the product he's building with the team. The company was still in stealth, and he just wanted to get my take on it and maybe if I can help him with the go to market. I saw the product, and from what I saw, basically, it takes what I used to do with my team in about eight months and does it in four to six weeks, and it saves customers money. When they say you hear the angels singing, so that's what I heard.
I'm like, "Oh." There was a lot of explicit. Oh my God. I almost fell off my chair, and I was like, "No, no, no. I'm going to work with you. I'm going to be one of the founders."
He's like, "No. I mean, we can't afford you. This is not going to work."
I'm like, "No, no, no." So, it was this dance of like, this is too good. It felt like I'm just in the burst of electricity, and I'm like, "No, I want to be part of this." So, fast-forward, it's been almost two years and it's been a lot of fun.

John Richards:
That's incredible. So my understanding, the area you all focus in is this data pipeline management, so for folks that are like, "What does this mean? Why do we need it?" what was it people were taking eight months to do that suddenly got shortened down to just weeks?"

Dina Kamal:
So basically, at a data pipeline product, we sit in the middle between all the security devices or security sources you have in your SIM. If we step back, so when I said I built a lot of security operation centers, that usually includes enhancing a SIM, upgrading a SIM, or building a new SIM capability. Within these projects, people think that what takes most time is creating new use cases and the configuration needed to do on the SIM product. What actually takes about two thirds of the time is the data integration work, to move the data from where it is to the right format that the SIM expects in an effective way.
That will take a lot of time from my team on doing custom connection, custom parsers, and doing a lot of engineering effort, which was quite expensive. I made a lot of money for my firm just by that engineering work, so DataBahn automates all of that. We have out of the box capability, basically a data engineer in our box that allows you to basically onboard a new data source into your SIM or any analytics capability, for that matter, with a click of a mouse. That's why when I saw it, I was in awe, so that's one part of it. I think the other very important part is we also have that knowledge, as a security practitioner that we've been using our SIM solution as a dumping ground for all our data, just in case we might need it, but that's a very expensive way to store your data, because SIM licenses are quite expensive.
We didn't really have the ability to tell what has value for security detection and what does not, so we end up just dumping everything. The issue with that is you're obviously overpaying for stuff you're not using, but you also run out of space to ingest additional data that you actually use for detection. So, the other element we have is, basically, having that content or knowledge, what has security value to send to a SIM, and the rest we can send up for storage for archiving and so on. So, it allows clients or organizations, basically, to maintain their compliance requirements, upload their SIM and save money, so I always say this is one example where you can have your cake and eat it too, if I may say.

John Richards:
Yeah. So, is the target then folks who don't have that process yet at maturity? That's obviously a target. Is there value for folks who already have the SIM build out to still adopt this practice?

Dina Kamal:
So, I always say that if you have a SIM solution, I think it's very important for you to think about, "Am I getting the value I want from it? Do I have the right data feeding into it?" And that's something actually we'll also do around doing that analysis again, is like the MITRE attack framework to say, "Are you actually collecting the right data to detect the threats that are most relevant to you?" And because we think that we just dump some data into the SIM, have some use cases enabled, and we're fine, but that is not enough, and it's not the right way to do it. We see some customers who are optimizing their costs, saying, "How can I actually do more with less?"
This is one way where you can cut on your license cost without impacting user detection capability, but also we're seeing many organizations saying, "You know what? I want to own my data, because instead of me shipping all my data to a cloud for SM vendor, I want to have a copy of all my data, so whenever I decide to go to a new SM vendor, a new MSSP provider, or I want to actually have another copy to run my own threat hunting analytics on it." So, we allow for that, being able to forge the data at multiple destinations, so the customers also having that ability to fully have full control on their data, especially as they consider additional capability, as I was saying, like Agentic AI, threat hunting, and so on.

John Richards:
Do you see a lot of folks needing to change vendors in the space that makes owning that data an important step there?

Dina Kamal:
Oh my God. There is about two to three cycle right now, so most organizations are churning their SIMs anywhere between two to three years, maximum five years, so having that ownership of your data, I think is very important because it becomes very challenging if you want to move your historic data into that new format. We've seen, actually, some vendor demanding to be paid to give a copy of the data that the customer already had, of the customer's data. So, having that prior copy, I think, is very important. By decoupling data collection, we always say that if you want to change your tap, you're not supposed to change all of your plumbing, so now with decoupling the tab from the plumbing, so if you want to change the tap to a fancier one and you are a better one, the plumbing, i.e. the data popline capability stays,.

John Richards:
And you're doing this without you all storing the data either, right? You're facilitating that through how you set up those pipelines and keeping a copy in their own location. It's not, "Oh. DataBahn has my data now." It's they own their actual data, right?

Dina Kamal:
Fantastic point. Yes, so we add a transit trail layer, we don't store any data, we don't keep a copy of the data. We just process it from one end, and we enrich the data to make sure that you're getting highly enriched data, either enriched by certain intelligence by your asset information or any other details you give us or by location, as an example, so that allows you to send to your SIM or analytics layer highly contextualized data, which means you have lower false positives, higher visibility, and you can have a copy of your own data in your environment. I call it just a very smart middleware, and that is easy to use.

John Richards:
So, can you speak a little bit about why people need it? I mean, you've been talking a little bit around this, but are there any use cases of, "Here's what went wrong," and if they had done this, they would have taken care of it?

Dina Kamal:
So many horror stories. We need a few podcasts for this. I think the first one is time to value, right? So, what does time to value means? First, if you have additional data sources you need to onboard, I think we accelerate that. As I was saying in my prior life, it used to take months. Now it takes a few days or weeks to do that data integration, so I think this is very important. The second point is we've seen some SIM vendors, and I will be careful not to name names, because that's not why we're here. Some SIM vendors are extremely hard to integrate with, which is really mind-boggling. I'm like, yes, the product works from a use case perspective, but if it's impossible to get data into it, I'm not sure how I can get value out of you. So, some customers come to us for that, because we make that integration very easy. We can have the data available in any format the customer wants.
I can blabber them like SEF, CSV, JSON, UGM, Sentinel, CIM from Splunk, so name the technology. I think by having that flexibility, then if you decide to choose a certain SEM technology or a certain data lake like Databricks, Snowflake, or what have you, or the new Sentinel Lake, we can make the data available to that destination in the format it needs very easily, and that really opens up the visibility for the customer. Many customers come to us, honestly, to save costs, because they're like, "I'm collecting so much data, and I know I don't need a lot of negative value out of it. So, by us being able to not only filter the data, but know what to filter, I think people miss that, that starting data to multiple destinations is important, but it's not enough, that knowledge around what has value pair data source and what does not at the event type. That is actually our intellectual property that came from years and years of experience.
For example, for firewalls, one of the banks we worked with, they have Splunk, and we're able to do the full implementation within six weeks and reduce the volume by 70%, without impacting the three detection. So, to have that kind of cost efficiency, if you will, especially in that economy, while being able to deliver the same value for the organization from a security perspective, I think it's quite powerful. We also have, for example, an airline. So, this airline had, for example, their black box data, so with their black box data, they had a separate SIM just from black box data, because they couldn't integrate this data into their same solution. Within an afternoon, using our AI capability that basically allowed the organization, basically we have an AI model that basically looks at the patterns in the raw data, identify the patterns, and give a purely usefully parse file in the format the customer wants. So, then afternoon, they were able to get the format required, and now they are able to feed their slogbox data directly into their SIM.

John Richards:
Wow, that's huge.

Dina Kamal:
I know. How cool is this? Yeah, I get excited about very nerdy things, but it's really cool.

John Richards:
Yeah, no. It's incredible, and you kind of teed up here, because I was going to ask. What led you to do this so much quicker than everyone else? So, it sounds like there's an AI piece. Is that really the secret sauce is, "Oh. We've got an AI model that's just better than everyone else," or is there a certain approach you take that shapes how that model works? What was it about this process that allows you to just skip so much time and be so efficient, where other folks are like, "We can't even get this to talk to that SIM, so we got to have a second one."

Dina Kamal:
I'll be very [inaudible 00:13:37] and say because we're super awesome, but then I'll give you the proper answer. I sit, as I mentioned in my intro, between the AI and the security universe. The reality of it is there are very few companies, practitioners, and engineers that actually understand that our AI engineers who understand cybersecurity, or cybersecurity engineers that truly know how to use AI at an engineering level, and that's what we have. I'll give you an example, so that's why it's both AI and that's deep cybersecurity expertise. Just to give you an example on this, one of the things we're working on is to have the data in an OCSF transformation.
So, to get the data into OSF transformation, the OCSF as a standard, Godless whoever did it, I don't want to see them in person, and maybe I'll strangle, them very convoluted. The thing has like 16 million fields, because it has different layers. It's very hierarchical, if you will, so it needs someone who really understand at the data engineering level, but also understands what fields the SIM would expect for transformation to work, and I know I'm being nerdy here, but that's level of understanding. Then, fine tune, configure, and create an agentic AI application that will have that level of understanding. So, you have to have the mix of deep data engineering expertise, deep AI expertise, but also really, really understand what does a cybersecurity organization expect. I can give you a few more examples if your brain did not blow up, I'm hoping.

John Richards:
Yeah. It may have blown up a little bit, but I would love another example, but also, I just want to comment that it's fascinating because everybody's worried about AI overtaking everything, but there is this place where what's really powerful is getting an incredibly knowledgeable expert with just deep knowledge in the space.

Dina Kamal:
Oh, 100%.

John Richards:
Using deep understanding of how AI works and being able to combine that, and then, as you said, layering on an understanding of the problem space. Really, when those three come together, you get something really unique, so yes, please share one more example. I'd love to hear it.

Dina Kamal:
No, no. You truly get magic. And someone, like I more like sometimes with the AI engineering effort, the AI tools we have right now are extremely lazy, so without that-

John Richards:
Yes.

Dina Kamal:
Oh my God. Oh my God. But so without that human in the loop, who actually has that expertise and that true knowledge to guide the AI model around what outputs we expect, how we should get there, and have the right guardrails, you really get smart looking garbage. Then, I see a lot of that where stuff looks really fancy, but I'm like, "I can't believe people are actually buying this, but I'm not going to go there." So, looking at that, the chatbot capability I mentioned, and we call it data reef.
So, a typical chatbot capability, it uses something we called RAG, so RAG stands for Retrieval Augmented Generation. Basically, you feed a document or a bunch of documents into the model, if you will, give it to the model as additional context. So for example, when I'm saying, what do you know about the stock? It basically finds the first match against that stock, and usually it's a similarity search. So I'm saying, "Look for Apple. It will look something that looks like an Apple, like the Apple word, and then come back to me." That usually works well, but the problem is, if I'm asking for 10.1.1.1, the model will for sure confuse that today with one to two, 10 to 10, because the similarities search.

John Richards:
Yeah, they're nearly identical. Why would they-

Dina Kamal:
They are nearly identical. So, my name is Dina. One of my colleagues [inaudible 00:17:55] is Aditia. If you use [inaudible 00:17:57] search, it actually thinks we are the same name, because we have D, I, and A in our names. So, what we needed to do is add that additional layer of retrieval as keyword search, and not any keyword search. The keyword search has to be trained on cybersecurity data, because when I say bot, bot for cybersecurity is different from bot in a typical natural language. When I say domain name, domain name for cybersecurity is different from domain name in a typical English language text, right? Then, we have a graph capability to basically stitch the data, because when I'm asking, "Tell me what you know about Dina," basically, I want to know about Dina, Dina's IP address, Dina's machine, Dina's host name, Dina's email.
So, from the persona of an investigator, when I'm asking about Dina, I'm asking about all her entity. That's why we have to do entity resolution, so you use graph database. So, I'm asking about Dina. The chatbot will give me all contextual information about Dena, not just the first match against that name, and so this is a lot more sophisticated and a lot more complicated. Again, that requires deep AI knowledge, but also deep understanding of graph technology and deep understanding of the user experience at the cybersecurity practitioner, because when I'm asking a cybersecurity investigator is different from asking at a cybersecurity compliance officer than asking the CISO, so we also create basically that experience based on the persona to give them the response that really is meaningful for them. That's really the art and the science of using AI and cybersecurity knowledge together.

John Richards:
The art and science, for sure, but both almost in equal measures. Now, so you've mentioned kind of two areas here. One, you're using this to allow you to onboard these integrations that are either impossible or take forever in rapid timelines, and then you're talking about this chatbot here that allows people to gain security insights. How do you do that when you're not storing the data? Are you at runtime accessing that, or is this based on the data you see just as it flows through? I'm trying to understand here if you're like, as you're monitoring what's going through the pipeline, that data key things are being pulled out, or if you're going back to the data and looking at it as the person asks questions?

Dina Kamal:
Oh my God. You're asking very technical questions. I'm very impressed. Yes. It's interesting that you caught on that, yes. So, for the first one, we can do the data on the fly, and we just need actually a data capture. We need a sample of the data, because some customers are not really ready to use AI yet. So, in that case, for that first use case for us to create AI enabled tarsters, if you will, we just need to have a sample data file, and that could be sanitized, because for example, we work with government organizations, so it's super sanitized because I just need to know the fields in the data file, and we'll dedicate the parser and then there is no AI from there, right? So, we also have another capability we're launching soon. Maybe I'm doing the GA before it's GA, like AI powered collector.
So, for example, if we want to pull in data or create a new API connectivity for a product, right now we have in our library 500 or so collectors to pull in the data from different sources, and some customers say, "Well, what if I have a product that you don't support yet?" Usually it takes between one to two weeks, which is quite fast compared to other vendors, but now we have this AI capability that actually do that on the fly. Basically, it takes five minutes to give you the YAML configuration file with the collector, and it's pretty cool. In that case, the only thing I need from the customer is the name of the product. That's it, and [inaudible 00:22:04] file will pop from there. So, these are ways that allow customers even who are not ready to adopt AI in their day-to-day to still get the benefits of AI, if you will, in terms of the ease of use streamlining and data engineering effort.
The other example from a security operation perspective, that chatbot, we actually start capturing the metadata. So that's why we call it a [inaudible 00:22:30], so we're not storing all the data. We're just capturing the data that we know will be most, we call them micro insights, so we're only capturing these micro insights that I know will be most relevant for a security investigator, whatever persona the customer asks us to provide in terms of data reef capability. So, it's meant to be the creme de la creme of the customer's data, if that makes sense, and we don't need to store it ourselves, we actually make that available on the customer's own environment. So we don't need to store the data anywhere.

John Richards:
What of that data do you see right now being most useful for your partners? I'm sure they're using it in a lot of wide ranges. That's the advantage of a chatbot, but is there certain pieces of data or insights that are unique to what you're being able to pull out this way, that people are like, "Oh, this really saved me time or saved me from some risk that I would've had otherwise"?

Dina Kamal:
So, I'll give you actually another example that we're using AI for that is so boring, I shouldn't say boring, but it's so powerful and important. It's for SOX reporting. Many organizations have to do that SOX reporting, and they have a lot of documentation that have SOX, different pieces of information, and need to pull all of this information. So, we have a number of our clients, where they have a team of four to six people who actually go through these reports, try to pull in the data, then put it in one Excel sheet, and try to make sense of it of this week compared to last week.
So, that manual effort is quite really horrendous, but it needs to be done, so what we do is we use AI to basically pull in the relevant data from all of those documentations, run analysis, and give them that beautiful dashboard in like no time, like in a few minutes. We can also easily do that trend analysis, depending on the different version that we received, or another use case around CISO dashboarding. I worked with a lot of CISOs, that their team can spend two weeks to create a monthly report because you have to collect all kind of data, but again, being able to, again, as a data highway, where we have visibility in all of the data, but also we don't have to collect logs. We can also ingest asset database. We can ingest ticketing information, certain intel data, PDFs, or documentation.
So, because we have the flexibility to ingest data in different formats, then we can do that level of analysis on it, and then also create a beautiful dashboard, because you can really use a mix of machine learning and old-fashioned data scraping, if you will, along with a nice Gemini to create a very fancy report for you or whatever LLM the customer is okay with. Again, we apply our understanding of the workflow of a SOC or a security organization, depending on the use case, and then figure out what are the right tools. We have a capability, again, we're launching in January that allow us to have that foundational capability in a low code, no code way, so the customer can actually empower to create that end-to-end agentic capability very easily to meet different requirements.

John Richards:
Yeah. You mentioned earlier, more with less. I don't know if that's like a company slogan or a mission or a value, but nobody gets into security to spend two weeks creating reports or fill out these SOX report.

Dina Kamal:
100 percent.

John Richards:
Even though it is very important, and so being able to take that off so that those teams can be focused on work that's really going to matter-

Dina Kamal:
Love that...

John Richards:
... Is huge. Yeah.

Dina Kamal:
100%, but that's a key point, because we're trying to use AI in a way that makes the security team's life easier so they can focus on higher value stuff. There is a lot of stuff, security practitioners stuff we do, I call them the brain numbing stuff, so we're trying to have DataBahn as a product, help you do the brain numbing stuff on your behalf quickly and cheaply, which I think is not a bad mandate to have. It's not a bad mission to have.

John Richards:
Yeah, no. That's huge. And so, we've put this in place, say, as an organization. What are the areas now that you're able to start getting visibility into and feel like, "Hey. This isn't out of control anymore"?

Dina Kamal:
It's actually interesting, because one of the things as soon as we get in, is the customer saying, "Oh my God. Now I have this so much additional capacity in my state. Oh, now I can get visibility into the subsidiary, that I have no clue what's happening there," or, "Oh. Now I can get actually visibility into my OT environment a lot better," or "I can actually expand my visibility into applicationally," or "Can you help me run some analysis with the visibility you have on maybe service accounts misuse?" So, there's different ways where we are able to show value very quickly. Again, the bottom line savings that the customer gets, it makes them very happy. We had a customer, actually, cried in our sales meeting last year. A few of our customers called in.
One of our customers said, "You guys made my Christmas, because I got a bonus because I was able to save a lot of money or our license."
I'm like, "Oh my God. I'm not used to my customers being so happy."
One of the stories I can share with you is we had one of our customers, they actually built about, I think, 12 applications. It's a financial services company, and they were supposed to go online, basically launch those applications or that capability within two weeks. Then, they realized that there is actually a requirement for them to have these applications monitored by the SIM. When they went to the engineering team around the effort to do that integration, they were told it would take six months. So, you can imagine the freak-out that happened because of that. The CISO gave me a call, and I was like, "Oh, no problem. No issues." I kid you not. By the next day, we had the integration figured out. It did take a couple of weeks, I would tell you, just going through the change to process with that organization, but it was done. I think they were able to go on time, so those things, by being at the right time at the right place, being that helpful, I think these are things that really makes our day as an organization, as DataBahn.

John Richards:
Yeah, no doubt. I love hearing satisfied customer stories, because it's really about solving challenges and problems people are facing, and when you're able to be like, "Oh. This person couldn't do anything. They were stuck," or is it going to take forever and you're able to solve it, that's huge. So, let me ask you, when should people be thinking about contacting DataBahn? Is it, "Hey. We're looking to really expand or start a SIM process practice at our organization?" What are the key moments, where it's the best time to reach out and say, "Hey. Let's have a discussion"?

Dina Kamal:
I think a few things. I think if you are looking to upgrade to an new SIM, you have a sim renewal, you're looking to acquire a new SIM, or to migrate a new one, as I mentioned. If you have a lot of application migration or application integration, if you have the significant issues around, how can I integrate all of this data into my analytics or same capability, that's where we can help. If you have an issue managing with your SOC cost, something obviously you can help with, and so, anything around data engineering, data integration, I think we can be helpful with.
The other thing also that, and again, we'll talk more about that in January, that Agentic AI capability in the SOC. We built a capability that is really very effective, very easy to use, and very cost-effective for organizations to build, to use Agentic AI in a controlled way within the security organization. So, more to come on that, but the triggers I shared with you and you mentioned, I think, will be good ones, or just reach out to us for a chat. You don't need a trigger. You can just reach out to us for a demo and just to see what those crazy guys are about.

John Richards:
Yes. Well, Dina, this has been incredibly informative. Thank you for coming on here and sharing, and I love hearing it.

Dina Kamal:
Thank you.

John Richards:
Before we wrap up though, what is the best way for them to learn more about DataBahn? Is it going to the website? What's the best way for them to kind of connect?

Dina Kamal:
Yes. Go to the website, databanh.ai. It's a very, very fulsome website. I always say we are an engineering company, so don't be overwhelmed with the amount of nerdy stuff in there. Databanh.ai, you can always find me on LinkedIn and I'll send you the right person, but I was saying, just reach out for a chat, and we're happy to nerd out and show you how we can be helpful for different organizations.

John Richards:
Amazing. I will make sure that we have those links in the show notes. Dina, thank you so much for coming here on the podcast and sharing your expertise. It's been wonderful getting to talk to you.

Dina Kamal:
Thank you so much. Thank you for your time, John, and pleasure talking to you.

John Richards:
This podcast is made possible by Cyberproof, a leading managed security services provider, helping organizations manage cyber risk through advanced threat intelligence, exposure management, and cloud security. From proactive threat hunting to manage detection and response, Cyberproof helps enterprises reduce risk, improve resilience, and stay ahead of emerging threats. Learn more at cyberproof.com. Thank you for tuning in to Cyber Sentries. I'm your host, John Richards. This has been a production of True Story FM, audio engineering by Andy Nelson, music by Ahmed Saghi. You can find all the links in the show notes. We appreciate you downloading and listening to this show. Take a moment and leave a like and review, and it helps us to get the word out. We'll be back February 11th, right here on Cyber Centuries.