SentinelOne is delighted to introduce Purple AI, a generative AI dedicated to threat-hunting, analysis and response. Purple AI uses a variety of models both open source and proprietary and aims to increase the organization’s efficiency by arming security analysts with an AI engine that can help identify, analyze and mitigate threats using conversational prompts and interactive dialog.

In this post, we explain how Purple AI will drive SOC team efficiency and efficacy in threat hunting, analysis and response and illustrate this powerful new feature with example use cases.

Threat Hunting Made Simple with Conversational AI

When it comes to threat hunting, building the right query to get effective results is not an easy task. It requires the analyst to understand what patterns to look for and be familiar with the query syntax at hand in order to translate a seemingly simple question into something the system can understand.

With Purple AI, analysts can now get rapid, accurate and detailed responses to any question, in any language, that otherwise would have required hours of research and multiple queries – not to mention years of analyst experience – to obtain an answer.

The Known Knowns

Purple AI allows threat hunters to ask questions about specific, known threats and get fast answers without needing to create manual queries around indicators of compromise.

For example, the analyst could use a prompt such as “Is my environment infected with SmoothOperator?”, or “Do I have any indicators of SmoothOperator on my endpoints?” to hunt for a specific named threat.

In response, Purple AI delivers a table of results along with context-aware insights based on the observed behavior and identified anomalies within the returned data. Suggested follow up questions and best next actions are also provided. In the case of the SmoothOperator example, a hunter might, for instance, receive the following summary:

The hunter can then follow up with additional questions suggested by Purple or manually typed by the user, such as:

The system will then automatically return results, alongside a summary of the identified behavior and recommend the next best investigation questions and response actions.

In the examples above, the final question might have resulted in a suggestion to trigger one or more of a combination of actions like “clear user session”, “suspend user`s Okta account”, “force Password reset”, “network quarantine all affected endpoints”, “create a rule to notify users on similar activity identified on other endpoints”, “collect recent security logs from affected machines” and more.

With a single click of a button the analyst can then trigger one or multiple actions, while continuing the conversation and analysis with Purple.

Purple runs on every piece of information within the SentinelOne Security DataLake and enables one-click response via the various SentinelOne XDR integrations. Every question asked by the analyst is executed against the right source or a combination of sources behind the scenes, without the user needing to be familiar with the various data sources or the way their data is ingested. For instance, analysts may ask:

The analyst can then trigger E/XDR actions like the following:

Or

The Unknown Unknowns

In other cases, however, threat hunters may not know what they are looking for. By leveraging the capabilities and speed of Purple AI to intelligently utilize internal and external resources, users can ask questions about suspicious activity they may have not been able to define themselves.

For instance, they might ask Purple AI to:

Or

Analysts can also leverage Purple to ask questions like “how can I identify X”? For instance:

Or

The above might seem simple, but without Purple AI, the task of translating vague terms like “sensitive data”, “commonly used” or “suspicious way” into patterns and then syntax of a query language that could return useful results is an extremely challenging  task.

Threat Analysis Made Simple

It is well-known that alert fatigue is one of the biggest challenges facing the modern security operations center (SOC). Most security teams receive more security alerts than they can possibly investigate and address. The issue is clear: the security problem is a data problem. Information only becomes knowledge once we apply meaningful linkages between multiple points of information, assembling the contextualized data into actionable results.

Purple AI, on top of its ability to help security teams ask complex threat hunting questions and run operational commands to manage the entire enterprise environment using natural language, also significantly simplifies the threat investigation process.

Purple’s understanding of ingested data as well as the cyber security domain allows it to quickly determine the chain of events and then to summarize a potentially complex situation to the analyst. The powerful combination of SentinelOne’s patented Storylines technology and Purple AI allows analysts to not only quickly find all events associated with a given activity but also get a summary of these events and their suspicion level in no time.

Within seconds, Purple will provide insights on the identified behavior alongside recommendations, thus reducing the need to manually analyze and stitch together diverse events into one contextual story. This vastly improves analysts’ efficiency, allowing them to investigate and triage a far greater number of alerts in significantly less time.

Purple AI In Action

Below is an example summary created for a threat identified by the SentinelOne Singularity platform.

Notice that Purple AI first indicates whether any malicious activity had been seen and where. This is important for the analyst to understand how widespread the attack is. In this example, all malicious activity had been identified on a single site. The user can then choose to drill down further to get detailed analysis of the identified activity if required.

Purple next provides detailed analysis of the events, indicating the behaviors that made SentinelOne classify the behavior as malicious.

In this case, a PowerShell process forced a group policy update, which triggered various indicators like Reconnaissance & Evasion. Purple then presents aggregations on the activity made by the suspicious process:

Purple will then move on to provide additional information on the entities associated with the malicious process, like users or files. Suspicious behaviors or attributes associated with these entities will be highlighted as well.

Finally, Purple summarizes the activity, highlighting the malicious entities involved, and provides a conclusion along with recommended next steps.

Now in possession of an accurate analysis with a high level of detail, the analyst is rapidly able to copy and paste the insights generated by Purple into a threat analysis report, or initiate further mitigation and incident response steps.

Conclusion

Purple AI is an integrated generative AI that allows threat hunters and SOC team analysts to leverage the power of LLMs (large language models) from within the SentinelOne console to identify and respond to attacks faster and easier. Using natural language conversational prompts and responses, even less-experienced or resourced security teams can rapidly expose suspicious and malicious behaviors that hitherto were only possible to discover with highly-trained analysts dedicating many hours of effort.

Purple AI will be delivered as part of SentinelOne’s threat-hunting experience and is  available in limited preview today. Contact us for more information or to request a demo.

Time to Patch | Google Issues Warnings for the First Two Zero-Day Vulnerabilities of 2023

Google has released emergency patches for two high-severity zero-day vulnerabilities affecting Chrome, CVE-2023-2136 and CVE-2023-2033, with the latter being actively exploited in the wild. Google is currently restricting access to further details until a majority of Chrome’s 3 billion users have applied the fix.

CVE-2023-2136 targets an integer overflow in Google’s Skia used in Chrome, allowing a remote attacker to perform a sandbox escape via a crafted HTML page if they compromised the renderer process. CVE-2023-2033 is a vulnerability targeting a confusion weakness in the Chrome V8 JavaScript engine. This type of flaw allows attackers to trigger browser crashes through reading or writing memory out-of-buffer bounds. It could also be exploited to run arbitrary code on vulnerable devices.

The latest version of the browser, v112.0.5615.137/138, includes a total of eight fixes. Currently, the stable release covers Windows and Macs and a rollout for Linux is scheduled to arrive in the coming days.

Data Exfiltration | Vice Society Ransomware Gang Uses New Stealthy PowerShell Tool

Notorious ransomware group, Vice Society, has been exercising a ‘rather sophisticated’ PowerShell script to automate data theft from compromised networks. The new tool employs living-off-the-land binaries and scripts (LOLBAS) designed to sidestep alarms from security software used by the targeted party so they can reach the encryption phase of the attack.

Researchers first observed this tool earlier this year when Vice Society began using a script named w1.ps1 referenced in a Script Block Logging event to exfiltrate data from a victim’s network. The script automates the data exfiltration process through multiple functions to identify vulnerable directories where data can be exfiltrated via HTTP POST requests to Vice Society’s servers.

Threat actors often leverage stolen corporate and customer data to extort a higher ransom from their victims and resell to other criminals for additional profit. Vice Society’s newest script allows them to operate stealthily and keep their footprint small – all signs of further evolution since debuting their new file encryptor, PolyVice, back in December 2022.

Critical RCE Flaws | Sandbox Escape PoCs Available for VM2 JavaScript Library

Three recent sandbox escape proof-of-concept (POC) exploits have been released, all enabling attackers to execute malicious code on hosts running VM2. A specialized JavaScript sandbox, VM2 is commonly used by pen-testing frameworks and code editors to run and test untrusted code in isolation. All three flaws have been assigned a critical scoring of 9.8.

CVE-2023-29017 describes a case where VM2 does not properly handle host objects passed to Error.prepareStackTrace during unhandled async errors. Using this, attackers could gain remote code execution rights. CVE-2023-29199 affects VM2’s source code transformer. If exploited, it allows attackers to bypass sandbox protections and gain remote code execution rights on the host running the sandbox. CVE-2023-30547 prevents the handleException() function from sanitizing exceptions discovered in the sandbox. By escaping these sandbox restrictions, attackers can perform arbitrary code execution in the host and potentially set up severe cyberattacks.

VM2 strongly recommends all users and developers using the VM2 library to upgrade to version 3.9.17 to address the security flaws.

LockBit Ransomware | New & Incomplete Samples of macOS Variant Surface

Researchers this week revealed details of a LockBit ransomware sample compiled for Apple’s macOS arm64 architecture. As of now, there are no reports of LockBit for Mac being exploited in the wild, nor any associated distribution method.

The discovered samples use “test” as a hardcoded password for execution, inviting speculation that the threat remains in its development stages. Researchers found that the Mac variant is a direct descendant of the Linux version, recycling much of the same code. Further, the Mac variant does not appear to be capable of exfiltrating the data it locks and has not been shown to have any method of persistence.

A breakdown of the variant shows that there is yet to be a credible threat to Mac endpoints at this time. Though the samples are underdeveloped, a LockBit spokesperson has said development of a Mac ransomware payload is an active project, raising concerns that more effective payloads targeting Apple Mac devices may be not far over the horizon.

Operation DreamJob | Tools Found in Linux Malware Found Tied to 3CX Supply Chain Attack

Operation DreamJob, a long-running campaign led by Lazarus group, has been observed targeting Linux for the first time this week. Using social engineering tactics to target job searchers on various platforms, victims are tricked into downloading malicious files disguised as files containing job opportunities.

After dropping malware on the victim’s device, a ZIP containing a Go-written Linux library is distributed masquerading as a PDF file, prompting victims to double click and launch the “OdicLoader” malware. A second-stage, C++ backdoor called SimplexTea is then launched.

The use of this backdoor and other common artifacts have resulted in researchers linking Operation DreamJob to Smooth Operator – the recent supply chain attack against VoIP provider, 3CX. The attack on 3CX has garnered major attention from the cyber defense community over the past four weeks; the most severe in the growing trend of supply chain attacks.

This Linux-based malware attack attributed to Lazarus is evidence of how threat actors are continuing to grow their arsenal and tactics, expanding their malware variants to target more systems than before.

On April 16th, Twitter user @malwrhunterteam tweeted details of a sample of the LockBit ransomware compiled for Apple’s macOS arm64 architecture. LockBit claims to be “the oldest ransomware affiliate program on the planet”, and news that one of the major cybercrime outfits in the ransomware landscape was now targeting macOS devices has predictably raised concerns about the ransomware threat on Mac devices.

In this post, we explore both the details of the LockBit sample uncovered and the larger question of how real is the risk of ransomware on macOS endpoints.

LockBit for Mac | Testing, Testing, 1 2 3

The sample of LockBit ransomware for Mac was discovered on VirusTotal on April 16th, and according to @vxunderground may have been compiled as early as 17th November 2022.

A further sample was uploaded to VirusTotal on the 8th December, 2022.

The macOS samples are compiled solely for the Apple ARM M1/M2 (aka Apple silicon) architecture. No macOS Intel sample is known at this time.

Importantly for concerned users, no occurrences of LockBit for Mac have yet been reported in the wild, no victims claimed, and no distribution method is known to be associated with the malware. However, early claims that the sample was non-functional were incorrect.

LockBit 3.0 typically requires a unique password to execute; in the case of the Mac sample, the hardcoded password is “test” – one of several clues as to the current state of development of the threat.

The Mac variant is a direct descendant of the LockBit for Linux variant first spotted in Jan 2022, and contains much the same code.

The ransomware functions as intended to encrypt targeted files, which are subsequently appended with the .lockbit extension. The locker also deposits a rather lengthy ransom note in the parent folder with the name !!!-Restore-My-Files-!!!.

The ransom note gives a clear indication of the intended victims.

LockBit is known for attacking and extorting organizations rather than random individuals on the internet, and the aim of the developers is to make large profits from locking and stealing business data.

The Mac sample does not appear to implement any functionality for exfiltrating the data it locks, nor does it have any method of persistence: more clear signs that this is a “work in progress” and not a genuine payload intended for use in the wild.

LockBit for Mac | Execution and Encryption

Despite the underdeveloped nature of the samples, it is clear that the authors are experimenting with similar functionality seen in lockers for other platforms. The malware is intended to be executed by a human operator or configuration file and offers a number of different encryption options. These mirror those seen in the Linux version noted above.

These can be seen reflected in the methods seen in the code.

Although there is a list of hardcoded extension names, many of which are not applicable to macOS, the locker is not restricted to encrypting only files with those extensions. As noted above, an operator may specify a particular destination and attempt to encrypt all files in that destination, partially or entirely.

Much of the code and methods apply to non-macOS platforms such as Windows, ESXi and Linux, indicating that the samples were likely compiled from the same cross-platform source code.

On execution on an Apple M1 or M2 device, the LockBit ransomware queries for the model name via sysctl hw.model, likely as part of anti-analysis measures.

Encryption takes advantage of the publicly available library Mbed TLS. Interestingly, there appear to be no cross-references to the functions intended to decrypt locked files.

According to one media report, the public-facing representative of LockBit, known as LockBitSupp, said that the Mac encryptor is “actively being developed”. Perhaps more complete samples may be over the horizon with the missing functionality.

Is Ransomware a Real Risk on macOS Today?

Due to the rampant nature of the ransomware threat on other platforms, it is only natural to wonder how safe Macs are from ransomware actors. While some security vendors have incorrectly made much of it in the past, the reality is that there is no publicly recorded case of any business ever paying a ransom demand as a result of macOS ransomware. This is not surprising when you look at the history of attempts to build ransomware on macOS to date.

The most recent known Mac ransomware prior to the LockBit sample found this week was EvilQuest (aka ThiefQuest). SentinelLabs analysis of the threat and subsequent publication of a decryptor revealed that the actual ransomware component was unfit for purpose. Despite garnering headlines like “New Mac Ransomware Is Even More Sinister Than It Appears”, not only was the encryption weak, the ransom note did not include a way for intended victims to contact the threat actors to exchange their money for a decryption key: It merely included a crypto wallet address and a demand for the princely sum of $50.

Unsurprisingly, that wallet remains empty today, having never received a single transaction. Though EvilQuest was certainly a real threat and continued to infect devices over the following 12 months or so, its infostealing and keylogging capabilities were likely the real reward for the threat actors: As ransomware, it failed entirely.

Prior to 2020, the next most recent macOS ransomware attempt was Patcher, discovered by ESET in 2017. Patcher (aka FindZip) was distributed in cracked apps on torrent sites and, much like EvilQuest, never recorded a single transaction to its bitcoin address.

Patcher was preceded in 2016 by KeRanger, a functional ransomware distributed through a trojanized version of the popular Transmission bittorrent client. KeRanger appears to have been precisely $3.02 more successful than either EvilQuest or Patcher ransomware: Its bitcoin wallet shows exactly one transaction – not necessarily from a victim – of that value.

We have to go back to 2014 to find the next occurrence of macOS ransomware. FileCoder was discovered on VirusTotal and was said to have been lying around on the site for two years. Unfinished and unworkable, FileCoder was a POC that did not actually encrypt the user’s files but demonstrated encryption and decryption of a sample file included with the malware. It is thought to be the precursor to the Patcher/FindZip malware discussed above.

In short, the history of ransomware on macOS to date shows that there has yet to be a viable threat or any ransomware that has financially impacted any individuals or organizations.

How to Stay Safe from LockBit macOS Ransomware

At the present time, SentinelOne does not consider LockBit a serious threat for macOS endpoints. As noted above, the known samples are very much a Proof-of-Concept and not suitable for deployment by threat actors in their current state. However, news outlets have reported that LockBit developers do consider a Mac file locker an active project, meaning that this situation may change in the near future.

As a precaution, the SentinelOne agent detects LockBit for Mac and protects macOS endpoints from executing the sample.

macOS security teams whose organizations are not protected by SentinelOne may refer to the Indicators of Compromise below for threat hunting and detection.

Conclusion

Are the big game hunters coming to a macOS endpoint near you? Not yet, but that doesn’t mean they won’t. The development of a Mac-specific ransomware variant suggests that the thought has obviously occurred to some threat actors sufficiently to invest time in producing a test sample, but that sample is far from ready for use against real targets. More importantly, from a threat actor’s point of view, locking files on Macs is not really a viable use case, though it may create some headlines, since service disruption in many cases is not likely to be severe – few organizations use Mac servers for essential services. In addition, worming from one Mac to another in the way Windows malware often does is exponentially more difficult on Macs. Consequently, the return on investment for a ransomware actor in deploying file locking malware on a Mac endpoint is likely to be substantially lower than similar attacks on Windows and Linux servers

Stealing data, however, is very much a viable use case for threat actors targeting Macs. As ransomware actors in general have transitioned to extorting enterprises to prevent stolen data being leaked, it should not come as a surprise if LockBit and other cybercrime outfits begin turning their attention to ways to achieve the same on macOS. Indeed, infostealers are very much a concern on the platform and security teams are advised to review our recent publications on these and on how threat actors can compromise Macs in the enterprise. If LockBit and other ransomware actors are coming for enterprises with macOS devices, it is the theft of data rather than the locking of files that will provide them with the most lucrative rewards.

Indicators of Compromise

YARA Hunting Rule

private rule Macho {
	meta:
		description = "private rule to match Mach-O binaries"

	condition:
		uint32(0) == 0xfeedface or uint32(0) == 0xcefaedfe or uint32(0) == 0xfeedfacf or uint32(0) == 0xcffaedfe or uint32(0) == 0xcafebabe or uint32(0) == 0xbebafeca

}

rule LockBit_for_Mac {

	meta:
		author = "Phil Stokes, @philofishal"
		description = "Rule to detect LockBit sample with Arm64 architecture for Apple M1"
		date = "18 April 2023"
		sha1_a = "2d15286d25f0e0938823dcd742bc928e78199b3d"
                sha1_b = "864f56b25a34e9532a1175d469715d2f61c56f7f"

		ref = "https:/s1.ai/LockBit-Mac"
	strings:
		$ransom = { 58 5b 55 5c 19 4b 58 57 4a 56 54 4e 58 4b 5c 19 } // encrypted ransom note string
		$sysctl = { 4a 40 4a 5a 4d 55 19 51 4e 39 5e 4b 5c 49 19 51 } // encrypted sysctl hw grep string
		$label = "bSelfRemove"

	condition:
		Macho and all of them

}

As cybersecurity threats increase in sophistication and frequency, the demand for skilled Security Operations Center (SOC) analysts continues to rise. In tandem with defensive strategies and advanced security software, SOC analysts fill a critical role in keeping enterprises safe from attacks.

SOC teams are responsible for identifying and mitigating oncoming threats, protecting sensitive information, and ensuring the overall security of an organization’s digital assets. As demand for skilled SOC analysts climbs, aspiring analysts need to ensure they have the technical knowledge, analytical skills, and critical thinking abilities required for the job.

This post is the first of three in a series covering the essential skills aspiring analysts should master as they embark on their journey toward success. In this post, we detail the first four key areas of study that lay the groundwork for mastery in the SOC analysis field.

1. Learn Network Architecture

Networking is the backbone of any IT infrastructure. For an aspiring SOC analyst, learning networking basics means understanding how data flows across the network; a skill critical in identifying and responding to security incidents.

By building a foundational understanding of networking concepts such as IP addressing, subnets, domain name system (DNS), routing, and protocols like TCP/IP, ICMP, and UDP, a SOC analyst can identify anomalies, track down malicious activity, and create effective security policies.

Since most attackers are initiated from the network, having a good grasp of network security fundamentals, including firewalls, intrusion detection and prevention systems (IDPS), and network segmentation provide SOC analysts with an edge in responding to security incidents. Understanding network fundamentals typically includes the below areas of interest:

• Learn networking fundamentals – Learn about network topologies, addressing, protocols, and networking devices. Resources such as the CompTIA Network+ or Cisco CCNA certifications can provide a solid foundation in fundamental networking concepts.
• Learn network security principles – Focus on firewalls, intrusion detection and prevention systems (IDPS), and virtual private networks (VPNs).
• Practice with hands-on labs – Use virtual labs or physical equipment to gain hands-on experience in configuring and troubleshooting networks. Examples include:
  • GNS3 – This free, open-source network simulation software allows users to design, configure, and test virtual networks.
  • Packet Tracer – This network simulation software developed by Cisco allows users to design, configure, and test network topologies.
  • EVE-NG – This network emulation software allows users to design, configure, and test virtual networks and complex network configurations.
  • TryHackMe – This platform provides guided, pre-configured labs accessible through a browser. The variety of high-quality courses and their low entry barrier allow learners to gain exposure to different tools and concepts.
• Join networking and security communities – Connect with professionals in the networking and security industry to learn from their experience, ask questions, and gain insights into the latest trends and technologies. Online communities such as Reddit’s /r/networking or /r/netsec, or professional associations such as ISACA, ISSA, or (ISC)², can be a great resource for connecting with others in the field.
• Stay up-to-date with industry news – Follow security and networking news sites such as Dark Reading, BleepingComputer, or SecurityWeek to stay informed on the latest security threats and trends.

2. Learn Network Analysis

Network traffic analysis involves examining the packets of data transmitted between devices on a network to identify patterns, anomalies, and signs of malicious activity. SOC analysts can detect suspicious behaviors such as unauthorized access attempts, data exfiltration, malware infections, and command-and-control communication by analyzing network traffic.

They can also use network traffic analysis to trace the origin of an attack, determine the scope of compromise, and identify affected assets. Network traffic analysis skills are key for any aspiring SOC analyst looking to build proficiency in threat detection and incident response. To get started on learning how to analyze network traffic, consider the below steps:

• Build up networking basics – Before analyzing network traffic, it is essential to have a solid understanding of networking concepts such as TCP/IP, DNS, HTTP, and SSL. Learn to interpret a packet’s structure and each header field’s role can help identify and troubleshoot network issues.
• Use network analysis tools – Various network analysis tools can help analyze network traffic, such as Wireshark, tcpdump, and tshark. These tools can be used to capture, decode, and analyze packets in real time or from saved capture files. Using Wireshark, for example, analysts can filter traffic by IP address, protocol, port, or keyword and analyze packet contents such as payload, headers, and timestamps.
• Practice analyzing network traffic – The best way to improve network traffic analysis skills is by practicing on real-world network traffic data. Sample capture files are obtainable from online resources such as the Wireshark Sample Captures page or by capturing traffic on a test network. Use the traffic to simulate an attack and create detection rules using a NIDS-like snort.
• Learn from online resources – Various online resources provide tutorials, blogs, and videos on network traffic analysis, such as the Wireshark University, PacketTotal, and the SANS Institute. These resources can help budding analysts learn advanced techniques like protocol analysis, network forensics, and malware analysis.

3. Learn Log Analysis

To be effective in their role, SOC analysts need to show proficiency in log analysis. Logs are a critical information source containing a wealth of data about system and network activity, user behavior, and security events. By analyzing logs, SOC analysts can identify suspicious activity, track the spread of malware, and detect potential security incidents.

Log analysis also plays a crucial role in incident response. When a security incident occurs, SOC analysts must investigate it, determine its scope and impact, and identify the root cause. Data captured in logs can help SOC analysts reconstruct the incident timeline, identify the attacker’s entry point, and determine the extent of the compromise.

Analyzing logs will also be required for any in-depth forensic investigations. The analysis involves examining logs generated by various systems and applications to detect anomalies, suspicious activities, and signs of compromise. Experienced analysts can detect events such as failed login attempts, unusual network traffic, and system changes that may indicate a security incident. Below are some methods aspiring analysts can take to improve their log analysis skills:

• Become familiar with log management tools – Log management tools like Splunk, ELK, and Graylog can help analysts to parse, search, and analyze logs. These tools can collect logs from different sources, apply filters and transformations, and visualize log data. Use these tools to view the organization’s security posture comprehensively.
• Learn common log formats – Logs come in a variety of formats. Learning common log formats like Syslog, Apache, and Windows Event Logs will serve to develop a stronger understanding of log data and how to make sense of it.
• Study log analysis, parsing, and search techniques – SOC analysts must have a wide arsenal of knowledge on log analysis techniques such as anomaly detection, correlation analysis, and threat hunting. Also, practice parsing and searching logs with different log management tools and techniques.
• Use regular expressions (Regex) – Regular expressions (regex) are a powerful tool for parsing and searching log data, allowing analysts to extract specific information from logs quickly.
• Filter noise – Logs may contain a lot of noise, such as debug messages, informational messages, or system messages. Filtering out noise helps analysts focus on the essential log data only.
• Use visualization tools – Visualization tools like graphs, charts, and dashboards are useful when trying to understand log data quickly. Utilize any visualization features within log management tools to create graphs or dashboards that show trends or anomalies in log data.
• Stay updated on threat news – Cybersecurity threats and attack techniques constantly evolve. Stay in the know with the latest cybersecurity news and trends. Follow industry blogs, attend webinars, and participate in online communities to stay informed.

4. Learn Endpoint Analysis

In today’s digital landscape, cybercriminals are continually devising new ways to exploit vulnerabilities and launch attacks, and the traditional perimeter-based security model is no longer enough. Security operations centers (SOCs) are at the forefront of identifying and mitigating these threats, and SOC analysts need to be familiar with a variety of tools and techniques to protect their organization’s network and sensitive data. Among these, one of the most critical tools that SOC analysts need to master is endpoint security tools.

Endpoint security tools protect against cyberattacks, focusing on securing endpoints like laptops, desktops, mobile devices, and servers. The endpoint is where an attack usually occurs, and it’s also the entry point for malware and other cyber threats. Endpoint security tools help to identify, isolate, and remediate the threat before it can cause significant damage.

Endpoint security tools in the hands of a knowledgeable SOC analyst can do the following:

• Protect vulnerable endpoints – Attacks on endpoints can result in data breaches, system disruption, and other security incidents. Endpoint security tools help to protect against these attacks by providing real-time visibility and control over devices.
• Perform advanced threat detection – Endpoint security tools use advanced threat detection mechanisms like behavioral analysis, machine learning, and artificial intelligence to detect and respond to threats. These tools can identify and isolate suspicious activities, providing SOC analysts with the information they need to respond to incidents quickly.
• Increase visibility – Endpoint security tools provide SOC analysts with a complete view of endpoint devices, including their applications and processes. This visibility allows analysts to identify vulnerabilities and misconfigurations that cybercriminals could exploit.
• Save time – Endpoint security tools can automate and orchestrate response actions, reducing the time it takes to detect and respond to incidents. This automation helps SOC analysts to focus on high-priority incidents, improving the overall efficiency and effectiveness of the SOC.

There are several endpoint security tools that SOC analysts need to master to protect their organization’s network and sensitive data. The most important of these are:

• Endpoint Detection and Response (EDR) – EDR solutions provide real-time visibility into endpoint devices, enabling SOC analysts to quickly detect and respond to incidents. EDR solutions use advanced threat detection mechanisms like behavioral analysis and machine learning to identify and isolate suspicious activities.
• Antivirus & Anti-Malware – These can help to protect against known threats. These tools use signature-based detection to identify and block known malware and viruses.
• Vulnerability Scanners – These tools scan endpoint devices for vulnerabilities and provide SOC analysts with a list of vulnerabilities that need to be addressed.
• Patch Management Tools – These are vital to keeping endpoint devices up to date with the latest security patches and updates. These tools protect endpoint devices against known or so-called “N-day” vulnerabilities.

Conclusion

The path to mastering the art of SOC analysis begins with these fundamental skills, but it does not end there. In Part 2 of this series, we will cover how analysts can develop further and explore more advanced topics including cloud computing, Active Directory, threat hunting and malware detection.

Armed with a solid understanding of these concepts, new and developing analyts can rapidly learn how to detect intrusions and isolate them before they move deep into a sensitive environment and create long-lasting damage.

A trained and experienced SOC analyst is an invaluable component of today’s cybersecurity defense. To build on the skills we’ve discussed in this post, look out for the next part of this series by subscribing to our email list or following us on social media.

SentinelOne offers robust Managed Detection & Response (MDR), Managed Threat Hunting (MTH), Compromise Assessment and Incident Response Services. To learn more contact us or visit SentinelOne Global Services.