It feels likes eye-popping times for those deep into building the future of visual data experiences. With Looker exiting (-> Google for $3B), Tableau exiting (->SalesForce for $16B), and less public, Periscope & ZoomData exiting, the Graphistry team is experiencing good feelings and key reflections. One of them is… the $16B exits are just a prelude to the next $160B in opportunities.
Learning to Whitebox the SOC-in-a-Box
Even as organizations automate their security operations with orchestration and AI, some of the most important parts of security investigations continue to depend on human analysis and talent. These critical moments in the investigation remain frustratingly slow, and need categorically different technologies that are optimized for human-in-the-loop analysis.
A balanced security strategy requires us to augment and extend human skills and abilities for the many daily tasks that we cannot trust to bots. This is one of the key goals at Graphistry, and we have previously described the fuzzy data aspect of the problem in our previous article, u201cSecurity in the Age of Maybeu201d. Orchestration and AI are important parts of modern security strategies, but we have to remember that analysts need to deal with them. This article digs into our experiences around the challenges and opportunities presented when orchestration and AI meet critical human-in-the-loop phases of an investigation.
Hurry Up and Wait
Security investigation workloads have outpaced the ability of organizations to hire analysts, so it is no surprise that teams are replacing people with programs for low-level and low-risk tasks. The interesting part, as in most things, is where automation stops short.
Security-critical workflows still often end in or depend on human-in-the-loop (HITL) analysis, and for good reason. Distinguishing real threats from false positives, understanding the true scope of an infection or intrusion, or pulling the thread to expose a hidden attacker are just a few examples where human analysis remains essential. The outcome of these investigations determines the real security of an organization, so tickets and projects remain a daily reality.
Unfortunately, these investigations often remain slow and laborious, and are where efficiency and insight can go to die. As soon as tools make the handoff to the human analyst, the process regresses by 15 to 20 years. We go from automated process to an analyst squinting at dashboards and writing command-line style search queries. In order to make security operations run faster, we need to bring the same ethos of automation, orchestration, and intelligence to the messier, more complicated iterative work of human in the loop analysis. If we don’t, then much of the anticipated benefit of investing in those tools could be lost in a case of u201churry up and waitu201d. This means that the speed, visibility, and reliability we gained through automation could be lost at moment it matters the most!
Augmenting Human Analysis
If we want to improve a human outcome, it makes sense that we design for and try to extend natural human skills. That is why Graphistry has made unprecedented investments into building best-of-class visual technology. Unlike programs, people understand information visually. Humans deal with enormous amounts of data and complexity every day when it is shown visually, and this is why we convert virtually any data into visual graphs. Using graphs we literally see the connections and relationships between our events, entities, and metadata. That could be seeing the progression of an attack along the kill chain or it could be seeing the layers of obfuscation within a money laundering scheme. In either case, a picture instantly reveals what would be relatively impenetrable if analyzed in a table of data.
Analysts are also wrestling with new types of data that may not always be intuitive. Machine learning and AI have become central to all types of analysis. The problem for many analysts is that the algorithms driving these models are often a black box that the analyst simply has to take on faith. Graph visualization has the power to provide analysts with the human UI into machine learning insights. Instead of looking at a generic alert reporting anomalous behavior, an analyst can actually see clusters, outliers, and complex relationships in the data. Likewise, the graph provides a direct visual interface for easily driving these systems, such as steering machine learning towards different parts of the dataset, and triggering actions on identified regions.
Leveraging Scale Without Letting It Get in the Way
The team at Graphistry has created a variety of core GPU technologies, which lets us unlock the needed flexibility to visually interact with large amounts of data. That includes simply seeing and understanding 100X+ more of our data in context. But since the final answer that we are looking for is often small, we also need to easily remove the noise and drill down or pivot to follow the intuitive flow of the investigation.
The goal is that we never want to limit the scope of an investigation, because we can’t see all of the important data, but at the same time we need to make sure the data doesn’t get in the way of seeing what’s really important. This is frankly where most see the difference between having a pretty picture and having a truly interactive investigation. Analysts need the ability to pivot across data sources on the fly, view events in the context of a timeline, or view data in the context of the network. Being able to do this without changing screens or writing new queries is critical for making sure analysts can investigate intuitively, creatively, and actually leverage the skills that make human analysts so valuable.
Automating the Human Workflow
In the previous topic, we were focused on improving our analysts vision: enable them to see more information, see deeper into relationships, and adapt on the fly. To close the loop, we need to focus on the speed of the workflow and how we accelerate those insights. Just because a workflow involves a human doesn’t mean that we can’t speed it up by orders of magnitude. This why Graphistry has pioneered the use of investigation templates and visual playbooks as a highly interactive investigation environment rather than rigid and hard-to-edit software.
First, a template allows an investigation to automatically begin with all the data that an analyst will need. With a trigger as simple as a single SIEM alert, Graphistry can automatically connect to and query any and all data sources to pull in the relevant context. This could be logs from other tools in the SIEM, NetFlow stored in a Spark cluster, and a variety of metadata from Bro logs in Elasticsearch. Without writing a single query, the analyst can right click on an incident, and all the necessary data is queried and prepared for analysis.
Crucially, that data is delivered through a highly interactive and visual workflow. Each step or pivot can have its own unique visualization setting tied to the needs of the analyst. Instead of being rigidly predefined, the analyst can tweak settings such as to look at a wider time range or find out more about a specific entity of interest, thus remaining fully interactive and explorable.
Organizations face a similar challenge when bringing orchestration into human-in-the-loop scenarios. Scripts should not be a blackbox that only other scripts can use. The visual graph and templates solve the human side of orchestration: analysts can simply click-and-fire!
This is just the beginning of what Graphistry does, but it hopefully serves to illustrate the path forward for security organizations. Analysts are some of the most critical assets in the enterprise, and it doesn’t make sense to simply automate around them. They need to be in the process. This is what we call turning the blackbox into a whitebox. To do so, we need to give analysts tools that augment their skills, and close the loop around automated workflows around data lakes, AI, and orchestration. At Graphistry, that is our mission.
Security is in the midst of a transformation that is putting extreme pressure on security analysts and hunt teams. One shift that is causing teams a lot of pain in their daily work is that as threats have gotten more sophisticated, security products have gotten much less sure of themselves. Security products increasingly detect the u201canomalousu201d and report threats on a sliding scale of confidence. Not only must staff deal with advanced threats, but they must spend an increasing amount of time navigating the grey areas and ambiguities of modern threat detections to determine and deliver the right actions.
Welcome to the Age of Maybe, where it is critical that we arm analysts for dealing with the indicators that are diverse, widespread…and uncertain.
Security in the Age of Maybe
It wasn’t so long ago that most of our security solutions were signature-based, network intrusions were relatively rare, and incident response was reserved for the few truly exceptional events.
But today, persistent attacks are the norm, not the exception. That means that IR has likewise become the norm, and many organizations proactively hunt for threats based on the statistically valid assumption that they are already compromised.
The problem is that while threats have gotten smarter and more common, security products have gotten less certain. Data science, machine learning, and AI have enabled security to see threats that would avoid traditional signatures, but the results are rarely cut and dry. Modern security products are increasingly powered by black-box algorithms that generate uncertain results. Is this anomalous behavior a threat or just an anomaly?
It falls to IR teams and hunters to turn this ambiguity into action. Security products report u201clikelyu201d or u201csuspectedu201d infections, give hints at a symptom of a greater incident, and report confidence in terms of percentages: these are too fuzzy to rely solely on automated actions. Despite all the progress in data-driven algorithms for finding hidden threats, almost no organization is willing to block and walk away without an analyst reviewing the incident and making the call. The net result is that every day more and more of the enterprise security stack is assuming their fuzzy alerts will go into the SIEM and someone will successfully pick it up and connect it to other activity: a human in the loop. As threats get ever more complex and security products follow suit, this is a problem that will keep is getting worse long before it gets better.
As a result, most IR teams are chronically overwhelmed with incidents and most organizations have realized they can’t hire enough staff to keep pace. Teams have naturally sought out ways to make IR more efficient often by automating and orchestrating IR process. This makes intuitive sense – if you are facing a manual bottleneck, then figure out how to automate it.
The challenge however is that IR and threat hunting aren’t just a robotic process of connecting logs and analytics to firewalls for enforcement. The critical step is still about human understanding and making smart decisions. Whether it is the team writing and maintaining the automations, or the responders dealing with what gets flagged, automation loops still involve an analyst loop.
It’s this human-in-the-loop part of the investigation where the magic happens, and it remains the most valuable in terms of stopping initial intrusions from turning into headline news, and the most time-consuming part of the IR process. It is also where innovation is needed the most. This is where Graphistry comes in. Instead of trying to turn analysts into bots, we arm analysts to get to better answers in a fraction of the time of a normal investigation. We add tooling to the human-in-the-loop flow to restore right balance between analyst and machine.
Getting a Grip on Fuzzy Data
The idea behind Graphistry is to provide analysts with a visual environment that brings together all of your security investments in unified and streamlined investigation. Graphistry is on a mission to knock out data bottlenecks in the human-in-the-loop analyst flow, one by one. Analysts can bring in as much or as little data as they need, see it all automatically correlated and mapped out, follow connections and pivot to new data sources on the fly, and drill down into event details when they need it. Using the power of graph visualizations, analysts get one-click visibility into event progression, correlations, and outliers in your data. Data is interactively visualized in analyst-friendly terms such as in the context of a kill-chain, timelines, network boundaries, and other perspectives that go beyond low-fidelity search and dashboard views
Our platform automatically handles the backend querying so that analysts can see connections across all of their security products, logs, SIEMs, threat feeds, and data sources without the need for complex manual queries.
Once we have the right answers, then Graphistry turns to automating the process. Investigations can be saved as repeatable best practices through the use of visual playbooks. These playbooks can act as a sort of interactive map to guide an analyst through a logical investigative flow. With each step an analyst can bring in new data sources and correlate or pivot using customized views of the data. Or instead of going step-by-step, analysts can run the entire investigation at once and render it all as single interactive visual flow. For investigations this often means vastly accelerating the u201ca-hau201d moment. Over time, more and more of a team’s investigations become fast, comprehensive, and reliable by covering them with Graphistry fastpaths.
This really only scratches the surface of what we do at Graphistry, and we haven’t yet talked about the technology that makes it all work. We’ll save that for another blog, but suffice it to say when you raise the visualization bar by 100x and deliver it all through commodity browsers, there is some interesting stuff going on on the backend. But ultimately the point of all that technology is to make life easier on the analyst. The role of the analyst is growing in organizations for a reason. Let’s focus on making analysts better instead of making them into bots.