June 12, 2021

How To Drive Value with Security Data – The Full Talk

Last week I keynoted LogPoint’s customer conference with a talk about how to extract value from security data. Pretty much every company out there has tried to somehow leverage their log data to manage their infrastructure and protect their assets and information. The solution vendors have initially named the space log management and then security information and event management (SIEM). We have then seen new solutions pop up in adjacent spaces with adjacent use-cases; user and entity behavior analytics (UEBA) and security orchestration, automation, and response (SOAR) platforms became add-ons for SIEMs. As of late, extended detection and response (XDR) has been used by some vendors to try and regain some of the lost users that have been getting increasingly frustrated with their SIEM solutions and the cost associated for not the return that was hoped for.

In my keynote I expanded on the logging history (see separate post). I am touching on other areas like big data and open source solutions as well and go back two decades to the origins of log management. In the second section of the talk, I shift to the present to discuss some of the challenges that we face today with managing all of our security data and expand on some of the trends in the security analytics space. In the third section, we focus on the future. What does tomorrow hold in the SIEM / XDR / security data space? What are some of the key features we will see and how does this matter to the user of these approaches.

Enjoy the video and check out the slides below as well:

June 8, 2021

A Logging History Lesson – From syslogd(8) to XDR

Category: Log Analysis,Security Information Management,Security Market — Raffael Marty @ 8:05 am

The log management and security information management (SIEM) space have gone through a number of stages to arrive where they are today. I started mapping the space in the 1980’s when syslog entered the world. To make sense of the really busy diagram, the top shows the chronological timeline (not in equidistant notation!), the second swim lane underneath calls out some milestone analytics components that were pivotal at the given times and the last row shows what data sources were added a the given times to the logging systems to gain deeper visibility and understanding. I’ll let you digest this for a minute.

What is interesting is that we started the journey with log management use-cases which morphed into an entire market, initially called the SIM market, but then officially being renamed to security information and event management (SIEM). After that we entered a phase where big data became a hot topic and customers started toying with the idea of building their own logging solutions. Generally not with the best results. But that didn’t prevent some open source movements from entering the map, most of which are ‘dead’ today. But what happened after that is even more interesting. The entire space started splintering into multiple new spaces. First it was products that called themselves user and entity behavior analytics (UEBA), then it was SOAR, and most recently it’s been XDR. All of which are really off-shoots of SIEMs. What is most interesting is that the stand-alone UEBA market is pretty much dead and so is the SOAR market. All the companies either got integrated (acquired) into existing SIEM platforms or added SIEM as an additional use-case to their own platform.

XDR has been the latest development and is probably the strangest of all. I call BS on the space. Some vendors are trying to market it as EDR++ by adding some network data. Others are basically taking SIEM, but are restricting it to less data sources and a more focused set of use-cases. While that is great for end-users looking to solve those use-cases by giving them a better experience, it’s really not much different from what the original SIEMs have been built to do.

If you have a minute and you want to dive into some more of the details of the history, following is a 10 minute video where I narrate the history and highlight some of the pivotal areas, as well as explain a bit more what you see in the timeline.

Thanks to some of my industry friends, Anton, Rui, and Lennart who provided some input on the timeline and helped me plug some of the gaps!

If you liked the short video on the logging history, make sure to check out the full video on the topic of “Driving Value From Security Data”

June 3, 2021

How To Drive Value with Security Data

Category: Log Analysis,Security Information Management,Security Market — Raffael Marty @ 8:55 am

We have been collecting data to drive security insights for over two decades. We call these tools log management solutions, SIMs (security information management), and XDRs (extended detection and response) platforms. Some companies have also built their own solutions on top of big data technologies. It’s been quite the journey.

At the upcoming ThinkIn conference that LogPoint organized on June 8th, I had the honor of presenting the morning keynote. The topic was “How To Drive Value with Security Data“. I spent some time on reviewing the history of security data, log management, and SIEM. I then looked at where we face most challenges with today’s solutions and what the future holds in this space. Especially with the expansion of the space around UEBA, XDR, SOAR, and TIP, there is no such thing as a standardized platform that one would use to get ahead of security attacks. But what does that mean for you as a consumer or security practitioner, trying to protect your business?

Following is the final slide of the presentation as a bit of a teaser. This is how I summarize the space and how it has to evolve. I won’t take away the thunder and explain the slide just yet. Did you tune into the keynote to get the description?

Interested in hearing more about log management and SIEM? Check out my history lesson post and video and the full video of the talk on “Driving Value From Security Data

May 19, 2021

Taking Inventory – Where Do We Stand With AI and ML in Cyber Security?

Category: Artificial Intelligence,Security Market — Raffael Marty @ 2:04 pm

Before diving into cyber security and how the industry is using AI at this point, let’s define the term AI first. Artificial Intelligence (AI), as the term is used today, is the overarching concept covering machine learning (supervised, including Deep Learning, and unsupervised), as well as other algorithmic approaches that are more than just simple statistics. These other algorithms include the fields of natural language processing (NLP), natural language understanding (NLU), reinforcement learning, and knowledge representation. These are the most relevant approaches in cyber security.

Given this definition, how evolved are cyber security products when it comes to using AI and ML?

I do see more and more cyber security companies leverage ML and AI in some way. The question is to what degree. I have written before about the dangers of algorithms. It’s gotten too easy for any software engineer to play a data scientist. It’s as easy as downloading a library and calling the .start() function. The challenge lies in the fact that the engineer often has no idea what just happened within the algorithm and how to correctly use it. Does the algorithm work with non normally distributed data? What about normalizing the data before inputting it into the algorithm? How should the results be interpreted? I gave a talk at BlackHat where I showed what happens when we don’t know what an algorithm is doing.

Slide from BlackHat 2018 talk about "Why Algorithms Are Dangerous"
Slide from BlackHat 2018 talk about “Why Algorithms Are Dangerous” showing what can go wrong by blindly using AI.

So, the mere fact that a company is using AI or ML in their product is not a good indicator of the product actually doing something smart. On the contrary, most companies I have looked at that claimed to use AI for some core capability are doing it ‘wrong’ in some way, shape or form. To be fair, there are some companies that stick to the right principles, hire actual data scientists, apply algorithms correctly, and interpret the data correctly.

Generally, I see the correct application of AI in the supervised machine learning camp where there is a lot of labeled data available: malware detection (telling benign binaries from malware), malware classification (attributing malware to some malware family), document and Web site classification, document analysis, and natural language understanding  for phishing and BEC detection. There is some early but promising work being done on graph (or social network) analytics for communication analysis. But you need a lot of data and contextual information that is not easy to get your hands on. Then, there are a couple of companies that are using belief networks to model expert knowledge, for example, for event triage or insider threat detection. But unfortunately, these companies are a dime a dozen.

That leads us into the next question: What are the top use-cases for AI in security?

I am personally excited about a couple of areas that I think are showing quite some promise to advance the cyber security efforts:

  1. Using NLP and NLU to understand people’s email habits to then identify malicious activity (BEC, phishing, etc). Initially we have tried to run sentiment analysis on messaging data, but we quickly realized we should leave that to analyzing tweets for brand sentiment and avoid making human (or phishing) behavior judgements. It’s a bit too early for that. But there are some successes in topic modeling, token classification of things like account numbers, and even looking at the use of language.
  2. Leveraging graph analytics to map out data movement and data lineage to learn when exfiltration or malicious data modifications are occurring. This topic is not researched well yet and I am not aware of any company or product that does this well just yet. It’s a hard problem on many layers, from data collection to deduplication and interpretation. But that’s also what makes this research interesting.

Given the above it doesn’t look like we have made a lot of progress in AI for security. Why is that? I’d attribute it to a few things:

  1. Access to training data. Any hypothesis we come up with, we have to test and validate. Without data that’s hard to do. We need complex data sets that are showing user interactions across applications, their data, and cloud apps, along with contextual information about the users and their data. This kind of data is hard to get, especially with privacy concerns and regulations like GDPR putting more scrutiny on processes around research work.
  2. A lack of engineers that understand data science and security. We need security experts with a lot of experience to work on these problems. When I say security experts, these are people that have a deep understand (and hands-on experience) of operating systems and applications, networking and cloud infrastructures. It’s unlikely to find these experts who also have data science chops. Pairing them with data scientists helps, but there is a lot that gets lost in their communications.
  3. Research dollars. There are few companies that are doing real security research. Take a larger security firm. They might do malware research, but how many of them have actual data science teams that are researching novel approaches? Microsoft has a few great researchers working on relevant problems. Bank of America has an effort to fund academia to work on pressing problems for them. But that work generally doesn’t see the light of day within your off the shelf security products. Generally, security vendors don’t invest in research that is not directly related to their products. And if they do, they want to see fairly quick turn arounds. That’s where startups can fill the gaps. Their challenge is to make their approaches scalable. Meaning not just scale to a lot of data, but also being relevant in a variety of customer environments with dozens of diverging processes, applications, usage patterns, etc. This then comes full circle with the data problem. You need data from a variety of different environments to establish hypotheses and test your approaches.

Is there anything that the security buyer should be doing differently to incentivize security vendors to do better in AI?

I don’t think the security buyer is to blame for anything. The buyer shouldn’t have to know anything about how security products work. The products should do what they claim they do and do that well. I think that’s one of the mortal sins of the security industry: building products that are too complex. As Ron Rivest said on a panel the other day: “Complexity is the enemy of security”.

Also have a look at the VentureBeat article feating some quotes from me.

April 16, 2021

A Security Product Reference Model

Category: Security Market — Raffael Marty @ 1:30 pm

On a recent consulting engagement with Cynergy Partners, we needed to decipher the security product market to an investment firm that normally doesn’t invest in cyber security. One of the investor’s concerns was that a lot of cyber companies are short-lived businesses due to the threats changing so drastically quick. One day it’s ransomware X, the next day it’s a new variant that defeats all the existing protective measures and then it’s a new SQL injection variant that requires a completely different security approach to stop it. How in the world would an investor ever get comfortable investing in a short-lived business like that?

In light of trying to explain the security product market and to explain that there are not just security solutions that are chasing the next attack, we developed a model to highlight the fact that security often needs to be deeply embedded into business processes. As a result, it becomes far more likely for security solutions to have a longer ‘shelf-life’. Here is the diagram that helps explain the concept:

Security Product Model

The diagram shows from left to right the technology evolution. You have legacy technology that is still running in organizations and drives businesses, for example your mainframes. Then you have current technologies and finally emerging technologies, such as 5G, IoT, AI, etc. All of the technologies have vulnerabilities that we learn about over time and we need to secure in some way. You can imagine that most every technology will need a different way to secure it, which creates the crazy complex ecosystem of security products and services.

With that setup, we end up in a world with three different types of security products, which

  1. Secure Business Processes
  2. Plug Security Vulnerabilities
  3. Enable Secure Software Development
As you can quickly see, the first and third type of security solutions are ones that do not change with the type of attacks or exploits. They are more technology and business use-case oriented. That also means that security products do not need to change drastically if new vulnerabilities are discovered or new attack methods are being used by adversaries.

Showing this diagram for our investment client helped them get more comfortable that they are looking at an investment that lives on the ‘steady’ or ‘sticky’ side of the security product spectrum where they do not have to worry about getting obsolete tomorrow just because the world of ‘attacks’ has changed into the next type of security exploits.

March 3, 2021

2021 Cyber Security Focus: “Unbound”

Category: Security Market — Tags: , , , – Raffael Marty @ 8:32 am

It’s already early March and the year is in full swing. Covid is still raging and we have been seeing some crazy weather patterns, especially in the south of the United States. While snowed in here in Texas, I took some time to reflect on what’s driving cyber security spend and customer focus this year. Overall, we can summarize the 2021 trends under the term of the “Unbound Enterprise“. You will see why when you look at the list of business drivers below. If you run a security business, you might want to see how your company caters to these trends and if you are in a role of protecting a company, ask yourself whether you are prepared for these scenarios:

Work from Home – The way that knowledge workers are doing their work has changed. For good. Most organizations, even after Covid, will allow their workforce to work from home. That brings with it an emphasize on things like endpoint security, secure remote access, and secure home infrastructure. The two big trends here from a market perspective are Secure Access Service Edge (SASE) and Zero Trust Network Access (ZTNA). Where the latter has initiated the long needed shift of focus to risk rather than event driven systems.

Supply Chain – Pretty much every product on the general markets is built from multiple supply sources; raw materials, specialized and integrated components. The production process is generally using tooling and machinery that is provided by another part of the supply chain. Think of third-party computer systems or MCU controlled infrastructure like HVACs, cloud infrastructures, and even external service personnel working on any of the infrastructure or processes of your company. Like most security challenges, securing the supply chain starts with visibility. Do you know which components are part of your supply chain? Who is the supplier and how trustworthy is said supplier?

SaaS Applications – Companies are moving more and more of their supporting infrastructure to third-party SaaS applications: Workday, Salesforce, Dropbox, even ERP systems are moving over to cloud services. Lower TCO, less maintenance headaches, etc. This means that not just backoffice services are moving to SaaS, but security product vendors also have to think about their product offerings and how they can provide SaaS enabled products to their customer base. Do it now. Do it today and not in three years when you have been pushed out of the market because you didn’t have a cloud offering.

Hybrid Infrastructures – Not all infrastructure will immediately move to the cloud. We will have to live through a time of hybrid infrastructures. The trend is for services to move into the cloud, but some things just cannot be moved yet for a myriad of reasons. This means that your security solutions probably have to support hybrid customer infrastructures for a while. Data centers won’t disappear over night.

Insider Threat – Insider abuse is a concern. Do you know how many of your engineers are taking source code with them when they leave the company? Generally it’s not a malicious act, but there is a certain degree of ownership that a software developer feels toward the code that they wrote. Of course, legally, that code belongs to the company and it’s illegal for the developer to take the code with them, but go check what’s reality. This translates into any job role. In addition, espionage is on the rise. The good news is that if you protect your critical intellectual property (IP), you can fend off not just insiders, but also external attacks as their goal is primarily to steal, modify, or destroy your data.

Board of Directors Cyber Committees – The regulatory environment has been pushing boards to pay more attention to the company’s security practices and procedures. The board is liable for negligence on the security side. Therefore, many boards have started cyber committees that evaluate and drive the security practices of the organization. Gartner predicts that 40% of boards will have a dedicated cybersecurity committee by 2025. How can we help these committees do their job? How does your security product help with surfacing and reducing risk to the company in a measurable way?

I hope these themes help you guide your security (product) organizations for the next bit to come. I’ll leave all of you who think about security products with a final thought:

Attack vectors (threats) are constantly changing. New vulnerabilities are found and exploited, new technology stacks have to be secured, and humans keep making mistakes when configuring systems, securing their data, and are prone to social engineering. With these constantly moving targets, what are the constants that we can (have to) secure in order to escape the broken cycle of security?

December 15, 2017

5 Security Big Data Challenges

Previously, I started blogging about individual topics and slides from my keynote at ACSAC 2017. The first topic I elaborated on a little bit was An Incomplete Security Big Data History. In this post I want to focus on the last slide in the presentation, where I posed 5 Challenges for security with big data:

5 challenges

Let me explain and go into details on these challenges a bit more:

  1. Establish a pattern / algorithm / use-case sharing effort: Part of the STIX standard for exchanging threat intelligence is the capability to exchange patterns. However, we have been notoriously bad at actually doing that. We are exchanging simple indicators of compromise (IOCs), such as IP addresses or domain names. But talk to any company that is using those, and they’ll tell you that those indicators are mostly useless. We have to up-level our detections and engage in patterns; also called TTPs at times: tactics, techniques, and procedures. Those characterize attacker behavior, rather than calling out individual technical details of the attack. Back in the good old days of SIM, we built correlation rules (we actually still do). Problem is that we don’t share them. The default content delivered by the SIMs is horrible (I can say that. I built all of those for ArcSight back in the day). We don’t have a place where we can share our learnings. Every SIEM vendor is trying to do that on their own, but we need to start defining those patterns independent of products. Let’s get going! Who makes the first step?
  2. Define a common data model: For over a decade, we have been trying to standardize log formats. And we are still struggling. I initially wrote the Common Event Format (CEF) at ArcSight. Then I went to Mitre and tried to get the common event expression (CEE) work off the ground to define a vendor neutral standard. Unfortunately, getting agreement between Microsoft, RedHat, Cisco, and all the log management vendors wasn’t easy and we lost the air force funding for the project. In the meantime I went to work for Splunk and started the common information model (CIM). Then came Apache Spot, which has defined yet another standard (yes, I had my fingers in that one too). So the reality is, we have 4 pseudo standards, and none is really what I want. I just redid some major parts over here at Sophos (I hope I can release that at some point).
    Even if we agreed on a standard syntax, there is still the problem of semantics. How do you know something is a login event? At ArcSight (and other SIEM vendors) that’s called the taxonomy or the categorization. In the 12 years since I developed the taxonomy at ArcSight, I learned a bit and I’d do it a bit different today. Well, again, we need standards that products implement. Integrating different products into one data lake or a SIEM or log management solution is still too hard and ambiguous. But you can learn doing this if you will look for Fortinet and learn how they do this.
  3. Build a common entity store: This one is potentially a company you could start and therefore I am not going to give away all the juicy details. But look at cyber security. We need more context for the data we are collecting. Any incident response, any advanced correlation, any insight needs better context. What’s the user that was logged into a system? What’s the role of that system? Who owns it, etc. All those factors are important. Cyber security has an entity problem! How do you collect all that information? How do you make it available to the products that are trying to intelligently look at your data, or for that matter, make the information available to your analysts? First you have to collect the data. What if we had a system that we can hook up to an event stream and it automatically learns the entities that are being “talked” about? Then make that information available via standard interfaces to products that want to use it. There is some money to be made here! Oh, and guess what! By doing this, we can actually build it with privacy in mind. Anonymization built in!
  4. Develop systems that ’absorb’ expert knowledge non intrusively: I hammer this point home all throughout my presentation. We need to build systems that absorb expert knowledge. How can we do that without being too intrusive? How do we build systems with expert knowledge? This can be through feedback loops in products, through bayesian belief networks, through simple statistics or rules, … but let’s shift our attention to knowledge and how we make experts by CCTV Melbourne and highly paid security people more efficient.
  5. Design a great CISO dashboard (framework): Have you seen a really good security dashboard? I’d love to see it (post in the comments?). It doesn’t necessarily have to be for a CISO. Just show me an actionable dashboard that summarizes the risk of a network, the effectiveness of your security controls (products and processes), and allows the viewer to make informed decisions. I know, I am being super vague here. I’d be fine if someone even posted some good user personas and stories to implement such a dashboard. (If you wait long enough, I’ll do it). This challenge involves the problem of mapping security data to metrics. Something we have been discussing for eons. It’s hard. What’s a 10 versus a 5 when it comes to your security posture? Any (shared) progress on this front would help.

What are your thoughts? What challenges would you put out? Am I missing the mark? Or would you share my challenges?

December 6, 2017

An Incomplete Security Big Data History

Category: Big Data,Security Information Management,Security Market — Raffael Marty @ 12:49 pm

Earlier today I was giving the keynote at ACSAC 2017. This year’s theme of the conference is big data for security. As part of my keynote, I talked about the history of big data in security. Following is the slide I put together:

Security Data History

This is by no means a complete picture, but I tried to pull together some of the most important milestones along the security data journey. To help interpret the graph, the top part shows some of the most important developments in security, while the bottom shows the history in the big data world at large. I am including the big data world as without the developments in big data, security would not be where it is today.

In addition to distinguishing between security and big data developments, I am using blue and green triangles to differentiate between ‘data collection or centralization’ (blue) and ‘data insight’ (green) milestones. I had a hard time coming up with too many ‘data insight’ milestones in both big data and in security. Seems we have a bit more work to do in our industry.

Let me make a few remarks about the graph. I’d be curious about your thoughts also; please leave a comment.

  • Security has been dealing with big data (variety, velocity, and volume) since 1996 – we just didn’t call it that back then.
  • We have been trying to apply anomaly detection to (network) security data for a long time. Interestingly enough, we are still dealing with a lot of the same issues as we had back then; one of them is having access to good training data.
  • We have been talking about security visualization for a long time. The first VizSec conference was held back in 2004. And in fact, we released the first versions of AfterGlow in early 2004.
  • In 2006 we released the first common data format, the common event format (CEF). CEF was a vendor driven approach (I co-wrote the standard while at ArcSight). Later in 2007, I approached mitre to help us take the effort public under the CEE banner. Unfortunately, that effort wasn’t super successful. Later I rewrote CEF at Splunk and we called it the common information model (CIM). Now we also have Apache Spot that released yet another standard data format. Which standard are we going to use? Well, for my own purposes, none of these standards really made the cut and I wrote yet another field dictionary at Sophos (to be released). The other problem with these standards is that none of them cover semantics, only syntax!
  • While deep learning had a big break through in 2009, we really only started using those algorithms in security in 2016. That’s probably a good thing. It’s just another machine learning algorithm that is actually really amazing at helping classify malware. But for a lot of other security problems it’s just not suited.
  • In 2014 I wrote the first version of the security data lake booklet. Most of the challenges I address in there are still applicable today. One of the developments that has helped making data lakes more realistic, are developments like Apache Drill or PrestoDB; or in general, the separation of data storage (e.g., parquet) from the query engines. These developments allow us to query our data lake with many different storage formats (CSV, JSON, parquet, ORC, etc.). It still requires a master data record to understand what we have and let us manage schemas across data sources.
  • Looking at database technologies, it is amazing to see what, for example, AWS has been providing with regards to tooling around data storage and access. Athena, RDS, S3, and to a lesser degree QuickSight, are fantastic tools to manage and explore large amounts of data. They provide the user with a lot of enterprise features out of the box, such as backups, fault tolerance, queueing, access control, etc.

What major milestones am I missing?

November 24, 2017

Security Chat 4.0 in Zurich

Category: Security Market — Raffael Marty @ 3:50 pm

Last week I organized the 4th iteration of the Security Chat – an informal gathering of security people in Zurich where we talk about all type of security from cyber security to home security using systems online, and you can learn more here about this. The format are 10-15 minute presentations that anyone can submit for. In good tradition, we had a great line up again:

  • Steve Micallef – OSINT and The New Perimeter – his tool is available for anyone to use at www.spiderfoot.net.
  • Ero Carrera – The Many Types of Machine Learning.
    Let’s not get carried away by the hype around deep learning. It’s great for some things, but not suited for many others. How about using Bayesian graph networks?
  • Stefan Frei – Supply Chain Security in The Digital Age
    Stefan gave us a bit to debate and think about when it comes to the security of the supply chain. Especially when it comes to Government procurement.
  • Maxim Salomon – Foobar on Embedded Systems
    Just how secure are our IoT devices out there? The presentation showed how insecure they really are. Even government grade security products!
  • Jeroen Massar – Don’t Forget The Network
    In his ‘no-slide’ presentation he made us remember that there are many aspects of networking that we tend to forget or a lot of people simply don’t know anymore. Things like BCP38, DNS RPC, etc.
  • Sandra Tobler – Next Generation Multi Factor Authentication
    More about the system that Sandra introduced can be found at: Futurae.com
October 12, 2015

Who Will Build the Common Backend for Security?

Category: Log Analysis,Security Information Management,Security Market — Raffael Marty @ 5:00 pm

 

VCs pay attention: There is an opportunity here, but it is going to be risky 😉 If you want to fund this, let me know.

In short: We need a company that builds and supports the data processing backend for all security products. Make it open source / free. And I don’t think this will be Cloudera. It’s too security specific. But they might have money to fund it? Tom?

I have had my frustrations with the security industry lately. Companies are not building products anymore, but features. Look at the security industry: You have a tool that does behavioral modeling for network traffic. You have a tool that does scoring of users based on information they extract from active directory. Yet another tool does the same for Linux systems and the next company does the same thing for the financial industry. Are you kidding me?

If you are a CISO right now, I don’t envy you. You have to a) figure out what type of products to even put in your environment and b) determine how to operate those products. We are back at where we were about 15 years ago. You need a dozen consoles to gain an understanding of your overall security environment. No cross-correlation, just an oportunity to justify the investment into a dozen screens on each analysts’ desk. Can we really not do better?

One of the fundamental problems that we have is that every single product re-builds the exact same data stack. Data ingestion with parsing, data storage, analytics, etc. It’s exactly the same stack in every single product. And guess what; using the different products, you have to feed all of them the exact same data. You end up collecting the same data multiple times.

We need someone – a company – to build the backend stack once and for all. It’s really clear at this point how to do that: Kafka -> Spark Streaming – Parquet and Cassandra – Spark SQL (maybe Impala). Throw some Esper in the mix, if you want to. Make the platform open so that everyone can plug in their own capabilities and we are done. Not rocket science. Addition: And it should be free / open source!

The hard part comes after. We need every (end-user) company out there to deploy this stack as their data pipeline and storage system (see Security Data Lake). Then, every product company needs to build their technology on top of this stack. That way, they don’t have to re-invent the wheel, companies don’t have to deploy dozens of products, and we can finally build decent product companies again that can focus on their core capabilities.

Now, who is going to fund the product company to build this? We don’t have time to go slow like Elastic in the case of ElasticSearch or RedHat for Linux. We need need this company now; a company that pulls together the right open source components, puts some glue between them, and offers services and maintenance.

Afterthought: Anyone feel like we are back in the year 2000? Isn’t this the exact same situation that the SIEMs were born out of? They promised to help with threat detection. Never delivered. Partly because of the technologies used (RDBMS). Partly due to the closeness of the architecture. Partly due to the fact that we thought we could come up with some formula that computes a priority for each event. Then look at priority 10 events and you are secure. Naive; or just inexperienced. (I am simplifying, but the correlation part is just an add-on to help find important events). If it weren’t for regulations and compliance use-cases, we wouldn’t even speak of SIEMs anymore. It’s time to rebuild this thing the right way. Let’s learn from our mistakes (and don’t get me started what all we have and are still doing wrong in our SIEMs [and those new “feature” products out there]).