What is Incident Detection Technology?

A security incident detection system is an automated process for identifying threats. The tools monitor and evaluate network traffic for abnormal patterns that indicate a breach.

According to Worksafe Traffic Control, AID systems use road networks equipped with detectors and CCTV cameras to conduct continuous monitoring. This helps traffic operators and stakeholders reduce the clearance delay for highway incidents based on accurate incident classification.

Security Information and Event Management (SIEM)

SIEM (pronounced sym) software provides enterprises with insight into what’s happening within their IT environments and a track record of how things have gone in the past. These powerful systems monitor signals in everything from host systems and applications to network security devices like firewalls, antivirus filters, and malware detection tools. The goal is to correlate those data points to identify patterns indicating a breach or other issues.

In order to work correctly, a good SIEM solution needs to be well-tuned to each customer’s unique IT environment and business objectives. This is done by creating and applying a library of customizable, predefined correlation rules. Defining the criteria that must be met in order to trigger an alert reduces the number of false positives generated by a SIEM system.

A quality SIEM system also enables threat prioritization, so high-risk events are flagged and addressed promptly, while low-risk activities can be offloaded to automated response processes. The right solution should also incorporate advanced analytics, such as the correlation of user identities with their network (IP) addresses and devices. This allows the solution to provide event context, which significantly improves threat detection and analysis.

Some of the more advanced capabilities available in modern SIEM solutions include automated responses, entity behavior analytics, security orchestration, and automation and response (SOAR). SOAR works by enabling an organization to connect its internal and external tools via built-in or custom integrations and APIs. Then, the tool collects and consolidates events from those sources and initiates a series of pre-planned responses based on defined incident analysis parameters and procedures. This is a powerful way to streamline the incident response process and minimize downtime caused by a breach or other issue.

Network Detection & Response (NDR)

NDR software is used to monitor an enterprise’s networks so that any suspicious activity can be detected and responded to automatically. This is done with the help of AI/ML-based tools. It also offers you the visibility that is necessary to take proper actions against threats like ransomware and other cyber-attacks.

Previously known as network traffic analytics (NTA), NDR solutions go beyond simple signature-based detection methods by monitoring network communications, not just endpoints or users. This enables the system to detect threats that may have evaded previous tools.

This is important because cybercriminals use a wide range of tactics to escape detection, such as lateral movement between different systems. This means they can hide the initial infected device behind a web of other devices to make it harder for security teams to find them. NDR technology can be used to identify lateral movements and alert security staff.

In addition to detecting threats that evade signature-based tools, NDR platforms are also designed to help detect threats that may have been hidden by encryption. These technologies can also provide visibility into networks that would otherwise be difficult to see, such as Internet of Things (IoT) and operational technology (OT) devices.

A key feature of NDR is the ability to prevent data exfiltration, which is the process of moving sensitive information outside of the network. This can allow attackers to steal proprietary data or extort money. The NDR solution can be used to keep an eye on sensitive data movement across the network and alert the security team if it is being moved illegally out of the company.

The NDR solution also provides visibility into traffic patterns that can reveal deception and evasion techniques used by attackers. This includes analyzing raw data and flow records, such as Net Flow, to construct models that reflect typical network behavior. An alarm is generated whenever the NDR solution detects deviations from these models.

NDR solutions offer a variety of additional capabilities that can be used to improve the effectiveness of security defenses, including automating incident response workflow processes and integrating with other security solutions. These features can help reduce an attack’s dwell time and improve overall cybersecurity protection for organizations.

Security Orchestration & Automation & Response (SOAR)

A key function of incident detection technology is to identify threats by actively monitoring assets (such as endpoints, networks, and applications) and looking for anomalous activity. When these are spotted, the software sends alerts and reports on them to security personnel for investigation. This is often done by a SIEM solution, which works symbiotically with a SOAR tool to help SOC teams prioritize and respond to cyber incidents.

Security orchestration is a major component of SOAR, which helps SOC teams address task-based challenges by combining automation and security orchestration on a single platform. Its main goal is to help organizations lighten the load on talent-constrained security teams by streamlining tasks such as vulnerability management and threat response through security automation.

Many SOCs use a variety of different tools for detecting and responding to threats, like firewalls, network detection and response (NDR) solutions, anti-malware, endpoint protection tools, and so on. Different vendors often provide these tools, making it challenging to get them to work together. SOAR platforms offer a central console that allows SOCs to integrate these tools into optimized security operations (SecOps) workflows and automate low-level tasks within those workflows using pre-defined playbooks.

The platform also unifies all the incident data from disparate systems or platforms, enriching them with context that security analysts can easily understand. This streamlines decision-making and enhances analyst productivity.

SOCs typically deal with hundreds or even thousands of security alerts daily. This can lead to alert fatigue, which may cause analysts to miss important signs of a threat. SOAR technologies make it easier to manage these alerts by unifying and prioritizing them in a single view, simplifying investigative workflows with automated actions that follow pre-defined playbooks, and speeding up response times through fully automated attack chains.

In addition, SOAR technology provides a set of tools to help SOCs identify, understand, and resolve threats, including auto-isolating a compromised device, running heuristics on endpoints to detect new malware, and identifying the source of suspicious activity. This enables SOCs to reduce the mean time to detection and the mean time to response, softening the impact of a breach on their business.

Artificial Intelligence (AI)

AI is computer software that aims to replicate human reasoning or complete tasks that would be difficult for humans. It’s an increasingly common technology that can be found in smart assistants like Apple’s Siri and Amazon’s Alexa; self-driving cars; augmented reality systems like Snapchat’s Lenses; and even the genomic sequencing of diseases. In fact, the technology is so widespread that a recent report from the Brookings Institution calls for new laws to ensure its ethics.

Unlike general preventive systems that give you alerts on deviant behavior, incident detection systems leverage contextual information to quickly detect threats. SIEM solutions, for example, use log correlation to look at patterns in security events from various sources and alert on unusual behavior. For example, if an employee accesses sensitive data outside of work hours, the system might detect this and alert the administrator to take action.

Other AID technologies use machine learning techniques to detect anomalies and solve the false positive problem. These include advanced heuristic algorithms that look for similarity to malicious code and other indicators of attacks; and behavioral threat analytics, which looks at how an attack is progressing based on the behavior of affected systems. These techniques allow AI to detect incidents sooner than human employees and previous technology iterations.

The field of AI has experienced rapid advancements, with notable milestones including IBM Watson’s victory on Jeopardy, the development of Deep Blue and Google DeepMind’s AlphaGo, and the launch of generative AI systems that create and improve images. However, there are concerns about the ethical impact of these advances: bias resulting from improperly trained algorithms and human bias; misuse of the technology – for instance, deep fakes or phishing; legal issues – such as AI libel; and data privacy issues – particularly in industries with strict regulatory compliance requirements (e.g., financial institutions).

Despite these challenges, the overall impact of AI is profound. It’s expected to drive innovation in areas such as transportation, logistics, healthcare, and agriculture by optimizing processes and making them more efficient. It’s already being used to optimize train schedules, predict delays; track airplanes and ocean shipping; and make food production more sustainable by predicting the weather, lowering the need for fertilizers and pesticides, and enabling weed control with robotic devices.