2022 Gartner® Magic Quadrant™ for APM and Observability Read the Report

Dave Frampton

Vice President and General Manager, Cloud SIEM & Security Analytics, Sumo Logic

Posts by Dave Frampton


XDR, what is it? Does everyone agree? What is real impact vs. hype?


Industry Analysts Recognizing Cloud Analytics Brings Wave of Disruption to the SIEM Market


Introducing Sumo Logic’s New Cloud SIEM Solution for Modern IT


Survey Data Reveals New Security Approach Needed for Modern IT

New World of Modern Apps and Cloud Create Complex Security Challenges As the transition to the cloud and modern applications accelerates, the traditional security operations center (SOC) functions of threat correlation and investigation are under enormous pressure to adapt. These functions have always struggled with alert overload, poor signal to noise ratio in detection, complex and lengthy workflows, and acute labor churn; however, cloud and modern applications add new challenges to integrate previously siloed data and process while coping with much larger threat surface areas. To overcome these challenges, security must continuously collaborate with the rest of IT to acquire and understand essential context. In addition, cloud and application-level insight must be integrated with traditional infrastructure monitoring, and investigation workflows must accelerate at many times the current speed in order to keep pace with the exploding threat landscape. In the past 2 months we’ve formally surveyed hundreds of companies about their challenges with security for modernizing IT environments in the 2018 Global Security Trends in the Cloud report, conducted by Dimensional Research in March 2018 and sponsored by Sumo Logic. The survey included a total of 316 qualified independent sources of IT security professionals across the U.S. and Europe, the Middle East and Africa (EMEA). In addition, we’ve interviewed a broad cross-section of both current and potential future Sumo Logic customers. According to the survey results, a strong majority of respondents called out the need for a fundamentally new approach for threat assessment and investigation in the cloud, and even the laggard voices conceded these are “if not when” transitions that will redraw boundaries in traditional security tools and process. In the Customer Trenches: Why Security and IT Must Collaborate Eighty-seven percent of surveyed security pros observed that as they transition to the cloud, there is a corresponding increase in the need for security and IT operations to work together during threat detection and investigation. Customer interviews gave color to this strong majority with many use cases cited. For instance, one SaaS company security team needed end customer billing history to determine the time budget and priority for conclusion/case queuing. Another online business process firm needed close collaboration with the cloud ops teams to identify if slow application access was a security problem or not. A third company needed IT help for deeper behavioral insight from identity and access management (IAM) systems. In all of these examples the heavy dose of cloud and modern applications made it nearly impossible for the already overburdened security team to resolve the issues independently and in a timely manner. They required real-time assistance in getting data and interpreting it from a variety of teams outside the SOC. These examples are just a few of the complex workflows which can no longer be solved by siloed tools and processes that are holding organizations back from fully securing their modern IT environments. These challenges surface in the survey data as well, with 50 percent of respondents specifically looking for new tools to improve cross-team workflows for threat resolution. This group — as you would expect — had plenty of overlap with the over 50 percent of respondents who observed that on-premises security tools and traditional security information and event management (SIEM) solutions can’t effectively assimilate cloud data and threats. Unified Visibility is Key: Integrating Cloud and Application Insight Eighty-two percent of those surveyed observed that as their cloud adoption increases there is a corresponding increase in the need to investigate threats at both the application and infrastructure layers. A clear pattern in this area was best summarized by one SOC manager, who said: “I feel like 90 percent of my exposure is at the application layer but my current defense provides only 10 percent of the insight I need at that layer.” Attackers are moving up the stack as infrastructure defenses solidify for cloud environments, and the attack surface is expanding rapidly with modular software (e.g. microservices) and more externally facing customer services. “Ninety percent of my exposure is at the application layer but my current defense provides only 10 percent of the insight I need” In the survey, 63 percent of security pros reported broader technical expertise is required when trying to understand threats in the cloud. An industry veteran who spent the past 3 years consulting on incorporating cloud into SOCs noted a “three strikes you’re out” pattern for SOC teams in which they could not get cloud application data, could not understand the context in the data when they did get it, and even if they understood it could not figure out how to apply the data to their existing correlation workflows. One CISO described the process like “blind men feeling an elephant,” a metaphor with a long history describing situations in which partial understanding leads to wild divergence of opinion. Customers interviews provided several examples of this dynamic. One incident pesponse veteran described painstaking work connecting the dots from vulnerabilities identified in DevOps code scans to correlation rules to detect cross-site scripting, a workflow invisible to traditional infrastructure-focused SOCs. Another enterprise with customer facing SaaS offerings described a very complex manual mapping from each application microservice to possible IOCs, a process the traditional tools could only complete in disjointed fragments. Many reported the need to assess user activity involving applications in ways standard behavior analytics tools could not. More broadly these cloud and application blind spots create obvious holes in the security defense layer, such as missing context, lost trials, unidentified lateral movement and unsolvable cases (e.g. cross-site scripting) to name a few. Diversity of log/API formats and other challenges make moving up the stack a non-trivial integration, but these obstacles must be overcome for the defense to adapt to modern IT. New Approach Needed to Break Down Existing Silos With all of these challenges in the specific areas of threat correlation and investigation, it’s no surprise that more generally an aggregate of 93 percent of survey respondents think current security tools are ineffective for the cloud. Two-thirds of those surveyed are looking to consolidate around tools able to plug the holes. A full third say some traditional categories such as the SIEM need to be completely rethought for the cloud. At Sumo Logic we’ve lived the imperative to bridge across the traditional silos of IT vs. security, application vs. infrastructure, and cloud vs. on-premises to deliver an integrated cloud analytics platform. We’re applying that hard won insight into new data sources, ecosystems and application architectures to deliver a cloud security analytics solution that meets the demands of modern IT. Stop by the Sumo Logic booth (4516 in North Hall) this week at RSA for a demo of our new cloud security analytics platform features, including privacy and GDPR-focused dashboards, intelligent investigation workflow and enhanced threat intelligence. To read the full survey, check out the report landing page, or download the infographic for a high-level overview of the key findings.


Three Dragons to Slay in Threat Discovery and Investigation for the Cloud

Chinese dragon symbol. Threat correlation and prioritization (what do I pay attention to in an avalanche of highlighted threats?) and threat investigation (how do I decide what happened and what to do quickly?) are extremely challenging core functions of the security defense, resulting in many cases with less than 10% of high priority threats fully investigated. The accelerating migration to cloud and modern application deployment are making these already difficult workflows untenable in traditional models, leading to questions such as how to gather and correlate all of the new sources of data at cloud scale? How to understand and triangulate new dynamic data from many layers in the stack? How to react with the pace demanded by new models of DevSecOps deployment? And how to collaborate to connect the dots across evolving boundaries and silos? Last week a veteran of many cloud migration security projects I know described many SOCs as“groping in the dark” with these challenges and looking for a new approach despite all of the vendor claims mapped to their pains. The usual crowd of incremental enhancements (e.g. bringing cloud data into the traditional SIEM, automating manual workflows, layering more tools for specialized analytics, leveraging wisdom of crowds, etc.) leaves three dragons roaming the countryside which need to be slain for security to keep pace with the unstoppable accelerating migration to the cloud. Dragon #1 – Siloed Security and IT Ops Investigation Workflows A basic dilemma in security for the cloud is that often the knowledge needed to pursue an investigation to conclusion is split between two groups. Security analysts understand the process of investigation and the broad context, but often only IT ops understands the essential specific context – application behavior and customer content, for example – needed to interpret and hypothesize at many steps in a security investigation. A frequent comment bucket item goes something like, “The SOC understands the infrastructure, but they don’t know how to interpret app logs or new data sources like container orchestration.” This gap in understanding makes real time collaboration essential to prevent exploding backlogs, partial investigations, and bias toward more solvable on-prem alerts. Aside from needing to understand unfamiliar, new, and rapidly changing data sources in a single security investigation, cloud deployments generate more frequent “Dual Ticket” cases in which it is unknown whether a security issue or an IT issue is the root cause (ex: my customer is complaining they can’t access our app – network congestion? Cloud provider outage? Server CPU overload? DDoS attack? Malware? Customer issue?) It isn’t just that two separate investigations take more time and resources to complete and integrate, often, in cloud cases, neither side can reach conclusion without the other. Working from common data isn’t enough – analytics and workflow need to be common as well to enable the seamless collaboration required. In addition, modern cloud deployments often employ DevSecOps models in which the pace of application update, rollout, and change is measured in days or hours as opposed to months or quarters. One security threat investigation implication is that the processing of the threat resolution backlog must align so that current resources can be applied to current environments without being mired in “old” cases or chasing continuous flux in the data. This is challenge enough, but having to manage this triage across two separate backlogs in both IT and security with the usual integration taxes means operating on the scale of hours and days is extremely challenging. While separate siloes for IT ops and security investigations were feasible and logical in on-prem classic IT, modern cloud deployments and application architecture demand a seamless back and forth workflow where at each step the skills and perspective from both IT and security are needed to properly interpret the results of queries, evidence uncovered, or unfamiliar data. Asking both sides to completely subsume the knowledge of the other is unrealistic in the short term – a much better solution is to converge their workflows so they can collaborate in real time. Dragon #2 – Traditional Security Bias on Infrastructure vs. Application Insight Traditional SIEMs have long been exhorted to look up the stack to the application layer, and in several instances new product areas have sprung up when they have not. In the cloud world this application layer “nice to have” becomes a “must have.” Clould providers have taken on some of the infrastructure defense previously done by individual companies, creating harder targets that cause attackers to seek softer targets. At the same time, much of the traditional infrastructure defense from the on-prem world has not yet been replicated in the cloud, so often application layer assessment is the only investigation method available. In addition to the defensive need to incorporate the application layer, there clearly is additional insight at that layer which is unknown at the infrastructure layer (e.g. customer context, behavioral analytics, etc.). This is particularly true when it is unclear whether a security or an IT problem exists. Many point systems specialize in extracting actionable insight from this layer, but the holistic correlation and investigation of threat is more difficult, in part because of wide variations in APIs, log formats, and nomenclature. Looking forward, modern application deployment in the cloud also increases the surface area for investigation and threat assessment. For example, chained microservices create many possible transitions in variables important to investigators. For all of these reasons, adding insight from the application layer is necessary and good for cloud deployments, but integrating this insight quickly with infrastructure insight is better. Many investigation workflows jump back and forth across these layers several times in a single step, so fully integrated workflows will be essential to leverage the assimilation of new insight. Dragon #3 – Investigation Times Measured in 10s of Minutes and Hours In cloud and modern application deployment, the sheer volume of incoming data will make yesterday’s data avalanche seem like a pleasant snow dusting. Also, dynamic and transient data, entities, and nomenclature make workflows straightforward (although still slow and annoying) in the old world (e.g. track changing IP addresses for a user or machine) extremely challenging in the cloud. Finally, collaboration will require new models of distributed knowledge transfer since investigation workflows will be shared across both security and IT ops. [Read More: Threat Intelligence] Many SOCs are at the breaking point in traditional environments with growing backlogs of investigations and reactive triage. Achieving investigation times in minutes to keep pace in the cloud despite these additional challenges, will require breakthrough innovation in getting rapid insight in huge dynamic data sets and in scaling learning models across both humans and machines. Slaying these dragons will not be easy or quick – new solutions and thinking will collide with comfort zones, entrenched interests, perceived roles of people and process, and more than a few “sacred cows.” Despite these headwinds – I’m optimistic looking ahead based on two core beliefs: 1) The massive economic and technological leverage of the cloud has already led to many other transition dragons of comparable ferocity being attacked with zeal (e.g. DevSecOps, Data Privacy, Regional Regulation, etc.), and 2) unlike many other transitions a broad cross section of the individuals involved in these messy transitions on the front lines have far more to gain in the leap forward of their own skills, learning, and opportunity than they have to lose. Aside from that, the increasingly public scorecard of the attackers vs. the defenders will help keep us honest about progress along the way.