Windows now displays in the properties tab of LNK files critical information that could reveal malicious code.
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Without proper security controls, AI agents could perform malicious actions, such as data exfiltration and malware installation.
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The Extended Detection and Response Platform (XDR) ingestion and correlation technology captures and correlates high-fidelity data across your security layers, such as endpoint, network, logs, cloud services and identities to...
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Over the years, endpoint security has evolved from primitive antivirus software to more sophisticated next-generation platforms employing advanced technology and better endpoint detection and response.
Because of the increased threat that modern cyberattacks pose, experts are exploring more elegant ways of keeping data safe from threats.
Signature-based detection is the use of footprints to identify malware. All programs, applications, software and files have a digital footprint. Buried within their code, these digital footprints or signatures are unique to the respective property. With signature-based detection, traditional antivirus products can scan a computer for the footprints of known malware.
These malware footprints are stored in a database. Antivirus products essentially search for the footprints of known malware in the database. If they discover one, they’ll identify the malware, in which case they’ll either delete or quarantine it.
When new malware emerges and experts document it, antivirus vendors create and release a signature database update to detect and block the new threat. These updates increase the tool’s detection capabilities, and in some cases, vendors may release them multiple times per day.
With an average of 350,000 new malware instances registered daily, there are a lot of signature database updates to keep up with. While some antivirus vendors update their programs throughout the day, others release scheduled daily, weekly or monthly software updates to keep things simple for their users.
But convenience comes at the risk of real-time protection. When antivirus software is missing new malware signatures from its database, customers are unprotected against new or advanced threats.
While signature-based detection has been the default in traditional antivirus solutions for years, its drawbacks have prompted people to think about how to make antivirus more effective. Today’s next-generation anti-malware solutions use advanced technologies like behavior analysis, artificial intelligence (AI) and machine learning (ML) to detect threats based on the attacker’s intention rather than looking for a match to a known signature.
Behavior analysis in threat prevention is similar, although admittedly more complex. Instead of only cross-checking files with a reference list of signatures, a next-generation antivirus platform can analyze malicious files’ actions (or intentions) and determine when something is suspicious. This approach is about 99% effective against new and advanced malware threats, compared to signature-based solutions’ average of 60% effectiveness.
Next-generation antivirus takes traditional antivirus software to a new level of endpoint security protection. It goes beyond known file-based malware signatures and heuristics because it’s a system-centric, cloud-based approach. It uses predictive analytics driven by ML and AI as well as threat intelligence to:
Today’s attackers know precisely where to find gaps and weaknesses in an organization’s network perimeter security, and they penetrate these in ways that bypass traditional antivirus software. These attackers use highly developed tools to target vulnerabilities that leverage:
To counter these attackers, next-generation antivirus focuses on events – files, processes, applications and network connections – to see how actions in each of these areas are related. Analysis of event streams can help identify malicious intent, behaviors and activities; once identified, the attacks can be blocked.
This approach is increasingly important today because enterprises are finding that attackers are targeting their specific networks. The attacks are multi-stage and personalized and pose a significantly higher risk; traditional antivirus solutions don’t have a chance of stopping them.
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Endpoint detection and response (EDR) software flips that model, relying on behavioral analysis of what’s happening on the endpoint. For example, if a Word document spawns a PowerShell process and executes an unknown script, that’s concerning. The file will be flagged and quarantined until the validity of the process is confirmed. Not relying on signature-based detection enables the EDR platform to react better to new and advanced threats.
Some of the ways EDR thwarts advanced threats include the following:
EDR agent software is deployed to endpoints within an organization and begins recording activity on these endpoints. These agents are like security cameras focused on the processes and events running on the devices.
EDR platforms have several approaches to detecting threats. Some detect locally on the endpoint via ML, some forward all recorded data to an on-premises control server for analysis, some upload the recorded data to a cloud resource for detection and inspection and others use a hybrid approach.
Detections by EDR platforms are based on several tools, including AI, threat intelligence, behavioral analysis and indicators of compromise (IOCs). These tools also offer a range of responses, such as actions that trigger alerts, isolate the machine from the network, roll back to a known good state, delete or terminate threats and generate forensic evidence files.
Managed detection and response (MDR) is not a technology, but a form of managed service, sometimes delivered by a managed security service provider. MDR provides value to organizations with limited resources or the expertise to continuously monitor potential attack surfaces. Specific security goals and outcomes define these services. MDR providers offer various cybersecurity tools, such as endpoint detection, security information and event management (SIEM), network traffic analysis (NTA), user and entity behavior analytics (UEBA), asset discovery, vulnerability management, intrusion detection and cloud security.
Gartner estimates that by 2025, 50% of organizations will use MDR services. There are several reasons to support this prediction:
The technology behind an MDR service can include an array of options. This is an important thing to understand when evaluating MDR providers. The technology stack behind the service determines the scope of attacks they have access to detect.
Cybersecurity is about “defense-in-depth” — having multiple layers of protection to counter the numerous possible attack vectors. Various technologies provide complete visibility, detection and response capabilities. Some of the technologies offered by MDR services include:
Extended detection and response (XDR) is the next phase in the evolution of EDR. XDR provides detection and protection across various environments, including networks and network components, cloud infrastructure and Software-as-a-Service (SaaS).
Features of XDR include:
Benefits of XDR include:
XDR is gaining in popularity. XDR provides a single platform that can ingest endpoint agent data, network-level information and, in many cases, device logs. This data is correlated, and detections occur from one or many sources of telemetry.
XDR streamlines the functions of the analysts’ role by allowing them to view detections and respond from a single console. The single-pane-of-glass approach offers faster time to value, a shortened learning curve and quicker response times since the analysts no longer need to pivot between windows. Another advantage of XDR is its ability to piece multiple sources of telemetry together to achieve a big-picture view of detections. These tools are able to see what occurs not only on the endpoints but also between the endpoints.
Security is constantly evolving, and future threats may become much more dangerous than we are observing now. We cannot ignore these recent changes in the threat landscape. Rather, we need to understand them and stop these increasingly destructive attacks.
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The need to achieve responsible enterprise security has taken center stage in enterprise IT management in recent years, precipitated by a deluge of public data breaches that damaged company reputations. However, lacking information on the most critical modern attack vectors, many organizations continue to rely solely on traditional virus scanning tools as their sole method of enabling endpoint security.
Many business professionals seem to cling to a common misconception that the implementation of a malware protection tool provides blanket protection against all potential security risks. The broad availability of free scanning tools and Window’s native Defender software has lulled individuals who are not particularly risk-conscious into a false sense of security when it comes to protecting their IT resources.
To be clear, it is certainly true that scanning and remediation tools for malware — including viruses, Trojans, ransomware and adware — continue to be critical components of any security arsenal. According to Enterprise Management Associates (EMA) research, 73 percent of surveyed organizations indicated they have been affected by a malware attack, and only 58 percent reported a high level of confidence that they can detect a malware incident before it causes a business-impacting event.
These challenges are only accelerating due to a new generation of advanced malware attacks that are designed to target specific environments or conditions and are more resistant to removal or cleanup. However, it is important to recognize that these threats represent only a portion of the total risks posed by the use of endpoint devices in modern business environments.
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Beyond the threat of malware infection, the broad reliance on distributed endpoint devices — including desktops, laptops, tablets, smartphones and wearables — poses a number of challenges to enterprise security assuredness. In traditional environments, endpoint devices (primarily desktops) and the applications and data they utilized were kept contained on controlled business networks.
Today, however, critical business IT services are distributed across numerous public and private cloud, web, and server-hosting environments. Additionally, the “mobile revolution,” which began a decade ago, introduced more portable endpoint devices, allowing users to access business IT services from any location at any time. The consequence of these foundational changes to IT service delivery is that there is no longer a secure perimeter within which business devices, applications and data can be protected. Instead, all IT services must be considered continuously at risk.
Unfortunately, many bad actors are far ahead of the curve in figuring out how to exploit a world of interconnected and poorly secured software and devices. Cryptojacking is a prime example of this. It occurred to some resourceful individuals that it would be much cheaper and easier to secretly leverage the processing power of millions of end-user devices by embedding code in common websites to perform free cryptocurrency mining activities, rather than to purchase and manage a dedicated server farm for this purpose.
As a result, the performance of business devices and, by extension, the productivity of business workers are being diminished to line the pockets of clandestine entrepreneurs. Additionally, the eminent portability of the most commonly used endpoint devices (tablets and smartphones) further reduces their inherent security. EMA research indicated that one out of every eight mobile devices and one out of every 20 laptops containing business data ends up lost or stolen.
These are only two examples of rapidly evolving endpoint security challenges that plague enterprise operations teams, and this trend is expected to accelerate with cyberterrorists leveraging the power of intelligence technologies such as machine learning to identify new weaknesses they can exploit.
EMA recently noted that the most frequent consequence of a security breach is not a malware infection, but compromised business data. We live in an age when information is a commodity that can be bought and sold through both legal markets and shadowy outlets. The latter, of course, is the greater concern with critical data — such as user access credentials, Social Security numbers, bank account information and other sensitive information — regularly being auctioned on the dark web. Cyberattacks are no longer designed just to be a nuisance; they are the cornerstone of a high revenue-generating industry.
There are three principal methods through which data is compromised on an endpoint:
Antivirus and other malware protection solutions can certainly help protect endpoint devices from related attacks, but they do very little natively to prevent data loss from other attack vectors. To responsibly ensure endpoint devices can securely perform business tasks, organizations must adopt a multifaceted approach to security that continuously monitors for inappropriate device activities and effectively controls access to enterprise data and resources.
To enable holistic visibility, configuration, status and contextual information should be collected on devices, processes and network activities. Intelligence technologies, such as analytics, language processing and machine learning, should be applied to collected details so that any potential security risks can be rapidly identified, and policy-based automated responses can be immediately implemented.
Of course, enterprise data is not a risk at all if it is never removed from secured locations in the first place. This can be accomplished with the use of resource isolation technologies, such as containerization, app wrapping, virtualization and browser isolation solutions. Data access and distribution controls are also enhanced with the introduction of strong identity and access management (IAM) capabilities. IAM platforms that are risk-based and governed by policy controls provide a strong first line of defense in any security implementation, particularly if they holistically leverage device information collected by endpoint and security management tools, as well as common intelligence technologies to accurately determine the level of risk associated with allowing an access event to occur.
Unified endpoint management (UEM) solutions designed to support all endpoints across an entire IT ecosystem offer the optimal platform from which to manage a diverse range of security processes. Comprehensive UEM solutions centrally support capabilities for data collection, reporting and alarming, data analysis, and automated response that are the hallmark of a responsible endpoint security approach. Solutions in this field are greatly advantaged if they can extend their security management capabilities through direct integrations with related platforms or by enabling integrations with the use of an API.
Effective endpoint security management requires a broad spectrum of key functionality that goes far beyond just malware detection, but with the right resources in place, organizations can ensure the secure utilization of enterprise IT services without unnecessarily limiting workforce productivity.
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