Enterprise Strategy Group Getting to the bigger truth. White PaperDetect and Stop Advanced ThreatsFaster to Reduce Security RiskSecureworks Crowdsources Threat and Tactics Intelligence for Fast andAccurate Behavioral Threat DetectionBy Christina Richmond, Principal AnalystJanuary 2020This ESG White Paper was commissioned by Secureworksand is distributed under license from ESG. 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk2ContentsExecutive Summary – Market Challenges . 3Tactics, Techniques, and Procedures . 3Applying Data Science Expertise to Threat Data. 3A Primer on Data Science Terminology . 3Combining Behavior- and Signature-based Detectors Speeds Detection . 4Applying a SOAPA Approach to Security Analytics and Operations . 4Improving Outcomes with SOAPA and Behavioral Threat Detection . 5Red Cloak Threat Detection and Response (TDR) Application. 7Tactic Graph Detectors for Behavioral Threat Detection and Response . 7Platform Architecture . 8The Bigger Truth. 8 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk3Executive Summary – Market ChallengesThreat prevention is a critically important component of a cybersecurity strategy, and most organizations invest abundantresources into security controls and processes in this area. Cybersecurity professionals are responsible for threatprevention, detection, and response. Threat prevention depends upon decreasing the attack surface with proper securityhygiene activities, but that isn’t always enough, as adversaries can circumvent these defenses with simple changes toevade signature-based detection. This allows them to compromise IT assets and can lead to extensive damage.This is where threat detection and response come into play. Beyond blocking known malicious behavior, organizationsmust collect, process, and analyze internal and external data, identify and investigate suspicious activities, and remediateproblems quickly before minor issues become major data breaches. The processes, tools, and personnel used for thesetasks are generally referred to as security analytics and operations and often reside in a platform architecture called asecurity operations and analytics platform architecture, or SOAPA (see Figure 1).Tactics, Techniques, and ProceduresCyber-adversaries often employ sophisticated attack tactics, techniques, and procedures (TTPs) in order to avoid detection,but they don’t change the foundation of the TTPs greatly over time. Adversaries don’t often create new tools or tactics.They use the same ones over and over. In fact, while there are millions of malware variants—far more than “goodware,” infact—there are only hundreds of tactics or techniques that are used routinely with subtle changes. And, in many cases,multi-stage attacks simply blend into benign IT activities. This forces organizations to constantly upgrade security analyticsand operations tools, skills, and processes to stay a few steps ahead of the hackers. Unfortunately, this can be extremelydifficult, as security analytics and operations are often limited by a lack of experience, resources, and skills; an assortmentof disconnected point tools; and manual processes. But there is the possibility for organizations to impact the adversarywhere it hurts, in the wallet, by forcing them to build new tactics.This white paper is focused on the challenges that cybersecurity professionals face with threat detection and response(TDR) and advancing solutions to these challenges using SOAPA, machine and deep learning, human analysis, automation,and behavioral playbook mapping. While automation is not yet mature enough to detect malicious activity, analyze theTTPs used, and respond on behalf of organizations with zero human involvement, Secureworks is innovating to helpcompanies get more out of automation, software, and intelligence with its newly launched Red Cloak Threat Detection andResponse platform. The platform is particularly adept at spotting advanced and unknown threats quickly by using datascience to uncover known tactics or behaviors. Critical human analysis must be augmented by software-based securityanalytics tools if the security industry is ever to get ahead of the adversary.Applying Data Science Expertise to Threat DataA Primer on Data Science TerminologyConfusion about the use of data science for security abounds because disparate capabilities such as threat analytics,machine learning (ML), deep learning (DL), and artificial intelligence (AI) are loosely and interchangeably used in marketingsecurity tools and services. Threat analytics attempt to understand where threats to assets exist and plan mitigationstrategies around that. ML, DL, and AI are used in threat analytics and can reduce the complexity of analysis performed byhumans. Threat telemetry, threat data, and threat intelligence are often used interchangeably though they are different.Telemetry is the process of recording and transmitting the readings of an instrument or device, which then becomes thethreat data that machines and humans analyze. Threat intelligence means that the threat data has been codified,contextualized, and correlated to make it actionable. However, not all threat intelligence is created equally. Data sourcesvary from commercial data feeds through in-house security technology telemetry to crowdsourced data from multi-tenantservices such as managed security services (MSSs). There are many ways to get a broad set of data to analyze, but more is 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk4not necessarily better. However, crowdsourced data can broaden the lens and if expert analysis is deployed, false positivesand alert noise can be reduced. The use of commercial off-the-shelf (COTS) analytics solutions without real-world expertiseisn’t sufficient. To detect and thwart attacks, solutions must be trained to look at the threat landscape across multipleverticals and environments and to build models that allow analysts to see adversaries based on their understanding of theattacker’s goals and tactics. It’s not enough to provide a query engine with vast amounts of data and hope for accuratedetection without a lot of false positives.Combining Behavior- and Signature-based Detectors Speeds DetectionSignature- and reputation-based detectors are foundational but fall short of accomplishing the mission to thwart theadversary because pre-knowledge of the threat posed is required. Rather, when threat detection employs both signatureand reputation-based detectors plus a category of analytics that are not representational like these static ones are, theopportunity to find unknown malicious content is greatly improved. Additionally, threat researchers must seek anunderstanding of attacker intent, capabilities, and the opportunity coveted to add context to the threat telemetry. Forexample, why would attacker X target organization/person Y? Is person Y simply a pass-through target to get to a largeracquisition such as partner or customer data? Attaining context is difficult across a layered security architecture withmultiple vendors, which creates a diverse security taxonomy.Historically, the security industry has applied a long-outdated defense-in-depth approach, layering multiple best-of-breedsecurity technologies in the architecture. Sadly, this has created a cacophony of device telemetry but no commonmethodology to understand it all. Managed security service providers (MSSPs), which see a broad set of threat data from adiverse universe of security vendors and telemetry, have had to create a common language with which to interpret,correlate, and codify the data. This is what Secureworks has named Defense-in-Concert, an approach that provides muchneeded context and intelligence to identify attackers based on behavior. Once a common language is established, bothhuman and machine analysis can assist in determining next steps.Applying a SOAPA Approach to Security Analytics and OperationsAs stated previously, the processes, tools, and personnel used for collecting, processing, and analyzing internal andexternal data, then identifying and investigating suspicious activities, and remediating problems quickly before a majordata breach occurs are generally referred to as security analytics and operations and often reside in a platformarchitecture ESG calls SOAPA (see Figure 1). 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk5Figure 1. Security Operations and Analytics Platform Architecture (SOAPA)Source: Enterprise Strategy GroupAccording to ESG research, the most cited reason that respondents invest in security, analytics, and operations is toimprove the ability to detect, contain, and remediate advanced attacks. 1 It is important to remember, however, thatSOAPA is a tool that depends on what type of data is ingested to orchestrate and analyze. The adage “garbage in, garbageout,” or “GIGO,” comes to mind. If the organization ingests a diverse set of telemetry with no common language tointerpret it, the results are compromised. Orchestration and analytics are key to improving process and are essential toaugment human analysis, but it is critical to utilize a defense-in-concert approach where all vendor telemetry is normalizedby a common taxonomy in order to speed operationalization of the data.SOAPA isn’t easy. The threat landscape is changing so quickly as to make it challenging to keep up with SOAPA trends.In addition to a rapidly changing threat landscape and regulatory complexities, organizations are impacted by operationalefficiency and cost. In fact, 30% of ESG research respondents state that the cost of operations is one of their top challengeswith SOAPA activities, making it the most common response.Improving Outcomes with SOAPA and Behavioral Threat DetectionHow then do we put SOAPA to work in security operations (SecOps) so that threat hunters, researchers, and responderscan work smarter and faster? SOAPA is AI/ML/DL’s assistant in rapidly discovering the attacker’s perspective, and moredeeply understanding the threat landscape and behavioral tactics. SOAPA can help put these pieces together for faster andmore accurate behavioral detection and help security teams stop playing “whack-a-mole.” Faster detection and responseimply the need for more sophisticated TTPs, which cause the adversary to go back to the design phase for malware, henceincreasing their cycle time.Source: ESG Research Report, Cybersecurity Operations and Analytics in Transition, July 2017. All ESG research references and charts in this whitepaper have been taken from this research report.1 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk6We’ve discussed the importance of how data is used. Large quantities of generated machine data can create a high volumeof false positives if the data captured isn’t relevant, isn’t properly codified, and isn’t correlated to the right tools, and ifcontext isn’t derived. Beyond this, consider that threat intelligence is static, and that learning is dynamic. Machines canlearn but not in the same sensory capacity as humans. The Cynefin framework in Figure 2 on detection and responseanalytics provides clear identification of this challenge. We can divide the problem and the solution into the “known” andthe “unknown.” In the known quadrant (upper left square), there are some threats we can see and others that may beseen or unseen but that have signatures defining them. Note that this Cynefin makes apparent that the unknown is amajority—three-quarters in fact—of the framework, demonstrating that more is unknown than known in securityanalytics. In the lower left quadrant, we may realize we have a problem but not know how to detect it, and this is wherethe industry may use tools such as data mining, predictive analytics, and supervised ML to show unknown relations amongevents. Conversely, we often do not know there is an issue, but detection through pattern matching (upper right quadrant)may provide user, network, or data anomalies that can be investigated. The worst analytics situation in this framework, ofcourse, is the unknown unknown threat, with limited to no ability to detect (lower right quadrant).Figure 2. Knowns and Unknowns in Security AnalyticsSource: Enterprise Strategy Group 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk7Red Cloak Threat Detection and Response (TDR) ApplicationRed Cloak TDR is a software offering from Secureworks that extends the company’s portfolio beyond its current MSS,consulting, incident readiness, and response offerings. It is a security analytics application leveraging Secureworks’extensive knowledge of the threat and known tactics employed. Red Cloak uses machine and deep learning to analyzeextensive data across the IT ecosystem—in the cloud, on-premises, endpoints, network, and other vendor-inclusivesources. This data is enriched by crowdsourced threat and tactics intelligence, incident response (IR) expertise, and insightsgathered from thousands of unique customer environments.The key features of the Red Cloak TDR are: Integrated Threat Intelligence garnered through the Secureworks Counter Threat Unit research team. AI-based Detections to reduce the number of alerts and potentially detect threats that current tools miss. Intuitive Investigation Workflows, which Secureworks designed for its own team of investigators. Enrichment of Alerts to provide context to inform faster investigations. Software-driven Response, which automates containment and prevention actions predetermined by Secureworks’ 20years of investigation experience. Endpoint Visibility to assist in detection of adversaries by behavior alone with endpoint detection and responsetechnology powered by behavioral analytics. Automated Correlation to provide event relationship across the security environment to corroborate a compromise. MITRE ATT&CK Mapping to align attacker activity with security alerts. Ask an Expert Chat, which provides the ability to hunt and remediate along with Secureworks experts or seek asecond opinion from the Secureworks security team in real time if the customer is stuck during an investigation.Tactic Graph Detectors for Behavioral Threat Detection and ResponseA key differentiator of Red Cloak TDR is the use of a growing portfolio of advanced detectors, specifically the Tactic GraphDetector for behavioral threat detection and response. When signature-based systems fail, quick insight into adversarytactics are necessary to detect and disrupt advanced attacks before they put customer data, valuable intellectual property(IP), critical operations, and company reputation at risk. Adversaries often use the same tactics repeatedly, changing minorvariables to avoid detection. In fact, as stated, there are millions of malware variants, but only a few hundred tactics thatare commonly used. These tactics require time and investment to blend into benign activities and evade detection.Red Cloak TDR software applies ML/DL to data gathered from Secureworks’ 1,000 incident response engagements eachyear, telemetry from the company’s extended IT ecosystem, the MITRE ATT&CK Framework, and threat intelligencesourced from 4,000 customers. The Tactic Graph Detector discovers attackers based solely on ML/DL software tounderstand common tactics, behavior in other incidents, the attacker’s goals, and rich contextual insights. This deeper levelof contextual insight can also drastically reduce false positives as Red Cloak TDR can differentiate between malicious andbenign behavior.Red Cloak TDR leverages a library of detectors, including the company’s exclusive Tactic Graph Detector, to spot advancedand unknown threats before they can cause damage. These detectors leverage Secureworks’ crowdsourced insights into 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

White Paper: Detect and Stop Advanced Threats Faster to Reduce Security Risk8tactics or behavior to recognize hidden threats based on a chain of events or behavior. This not only allows faster detectionbut also helps organizations stop attacks earlier in their lifecycle. It also challenges attackers’ commonly used approaches,potentially forcing them back to the drawing board to create new and different tactics. When an adversary is pushed torecreate tactics, it hits them where it hurts: the wallet.Platform ArchitectureRed Cloak TDR is built for rapid innovation and is evolving into a platform-as-a-service with a broader set of applicationsand services. The platform can be purchased two ways: As a SaaS platform for use by an organization’s internal IT securityteam or SOC. Or as part of a managed service—managed detection and response. Secureworks will continue to deliver arange of services—managed security services, consulting, incident readiness, and response.The Secureworks Counter Threat Unit research team and IR teams have long sought to follow the 4Cs of threat detectionand response: capture, codify, contextualize, and correlate. For Red Cloak TDR, Secureworks has captured and culledthrough 20 years of threat intelligence, operational experience, and IR engagements, and codified the resulting insightsand threat data using machine-learning-based detectors, informed by Secureworks’ knowledge of threat actor TTPs.Additionally, Secureworks’ human analysts have added insight and analysis using behavioral playbook mapping. Thetelemetry is contextualized using real-world investigation insights from the company’s deep bank of IR engagements.Three delivery levels for Red Cloak TDR allow independent action by security analytics teams, partnership withSecureworks analysts on investigations, or outsourcing to Secureworks to perform analysis and investigation. Finally, the“Ask an Expert” function provides additional resources to help customers during an investigation irrespective of thedelivery level.The Bigger TruthThreat detection and response is difficult because attackers are so often ahead of defenders, forcing organizations toupgrade security analytics and operations tools, skills, and processes to try to gain the lead. Security analytics andoperations teams suffer a lack of experience, resources, and skills, and they work with disconnected point tools andmanual processes. Utilizing SOAPA capabilities speeds analytics when combined with valuable threat data, advancedbehavioral mapping, and additional human expertise. With these benefits, there is a greater likelihood of forcing theadversary to build new tactics, techniques, and procedures, which in turn increases their time to exploit. Secureworks RedCloak TDR is built on SOAPA and intended to bolster the customer’s human analysis strengths with software-based securityanalytics tools. Additionally, Secureworks brings 20 years of experience and crowdsourced threat data to inform behavioraldetection for faster, more accurate prevention, detection, and remediation of advanced and unknown threats using datascience. 2020 by The Enterprise Strategy Group, Inc. All Rights Reserved.

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Threat analytics attempt to understand where threats to assets exist and plan mitigation strategies around that. ML, DL, and AI are used in threat analytics and can reduce the complexity of analysis performed by humans. Threat telemetry, threat data , and threat intelligence