A Comprehensive Guide to Threat Detection

The diagram depicts the process of threat detection and response through three steps: Monitor, detect, and respond.

What is Threat Detection and Response?

SaaS threat detection and response includes two parts: monitoring continuously to identify the threat, and then responding.

The first segment of threat detection requires understanding your environment and the potential threats it faces whether to the confidentiality, integrity or availability of data within a given system. This sets the correct scope for developing detective controls via monitoring rules that automatically scans data and alerts teams to the presence of potentially malicious activity stemming from a cyberattack.

Alerts generated from automated monitoring leads into the second segment of response. Analysts or autonomous systems review alerts and investigate the data further looking for confirmation of the legitimacy of the alert. When the outcome of the alert is a false positive, the feedback loop ensures that automated rules are tuned to ignore noise from benign activity.

What is Advanced Threat Detection?

Advanced threat detection and response counters cyber threats with modern technologies, methodologies, and capabilities. Both automated, passive tools and more active, human-centered strategies are important to SaaS threat detection and prevention:

Automated/passive tools

Machine learning algorithms and AI. ML models analyze large volumes of historical data to detect advanced threats in real-time. This allows them to improve the accuracy and efficiency of the threat detection process.

Endpoint detection and response (EDR). Advanced threat detection tools often integrate with EDR solutions. These monitor endpoint behavior in real-time, uncover suspicious activities, and facilitate forensic analysis to detect threats and enable a rapid response to endpoint-related security incidents.

Automated responses to security incidents. Use available tools to speed the organizational response to cyber threats, including: quarantine/isolation for infected or compromised endpoints and systems, blocking malicious IP addresses, and triggering remediation workflows based on set criteria.

Integration with security orchestration, automation, and response (SOAR). Integrated SOAR platforms allow more efficient threat response at scale.

Active/human-centered strategies

Behavioral analysis techniques. Identifying abnormal patterns and deviations in account behavior, network traffic, application activity, and system processes enables the detection of compromise and threats that traditional signature-based approaches may miss.

Threat hunting. This is a proactive search for hidden threats and signs of compromise within the network that demands human expertise, threat intelligence, and specialized tools.

Deception technologies. These technologies such as honeypots, decoy assets, and breadcrumbs lure attackers into engaging with fake systems and collect valuable threat intelligence. Deception can help detect intrusions early, gather information about attacker tactics, and divert adversaries away from critical assets.

Threat intelligence fusion. Aggregated threat intelligence from internal telemetry, open-source intelligence (OSINT), industry feeds, and proprietary feeds provides a more comprehensive view of the threat landscape.

How Does Threat Detection Work?

Comprehensive enterprise threat detection combines technologies and human expertise to continuously monitor for indicators of compromise to identify, analyze, and mitigate potential threats in real-time:

Continuous monitoring. A constant stream of data from various sources throughout the IT environment, including network traffic, logs, endpoint activities, user behavior, and external threat intelligence feeds, fuels real time threat detection.

Active threat detection techniques. Signature-based anomaly detection, behavior analysis, machine learning, and threat intelligence all help identify potential threats and existing security issues, whether they were previously known or are novel.

Event correlation and threat detection analysis. Correlating detected threats and alerts for analysis allows IT teams to determine their severity, context, and potential impact on the organizational security posture.

Incident investigation. Upon detecting threats, these systems initiate a process to investigate the nature and scope of the issue. Analysts and/or managed threat detection tools conduct forensic and root cause analyses and gather evidence to assess the extent of the compromise.

Response and mitigation. Determine the source and nature of any threats including all appropriate responses. Responding to threats may include isolating or terminating compromised systems or processes, applying security patches or updates, blocking malicious IP addresses or domains, and restoring affected data from backups. Additionally, eradicating attacker artifacts, account recovery, rotating compromised credentials.

Adaptive security controls. Solutions for real-time threat detection often dynamically adjust security policies, configurations, and controls based on real-time risk assessments. This helps to proactively strengthen defenses and minimize the attack surface.

Intelligence integration. Cloud threat detection and response systems integrate with threat intelligence feeds to enrich analysis and improve detection accuracy. Threat intelligence offers valuable context about known threats, attacker tactics, techniques, and procedures.

Continuous threat detection and improvement. To succeed, the process should be iterative, involving continuous improvement based on careful analysis of past security incidents and emerging threat trends.

What Are 4 Methods of Threat Detection?

While there are numerous techniques aimed at identifying and responding to potential security threats and malicious activities within the IT environment, they can generally be categorized into four main types of threat detection:

Signature-based detection. Comparing observed data such as network traffic patterns against predefined rules or patterns of malicious activity to identify threats is effective for detecting known malware and attack patterns. However, it may struggle to detect novel or polymorphic threats that do not match existing signatures.

Anomaly-based detection. This identifies deviations from normal behavior or baseline patterns within the IT environment that might indicate a security threat. Anomaly threat detection management can uncover previously unknown threats, insider threats, and zero-day attacks, but it may also generate false positives as the complexity of environments grows and general account usage patterns shift over time.

Behavior-based detection. This kind of monitoring for threats detects lateral movement, privilege escalation, and data exfiltration—issues that may evade other defenses—but requires advanced analytics and deep contextual understanding of typical user, application, and system behavior.

Threat intelligence-based detection. External feeds and indicators of compromise can help identify known malicious entities to proactively block such as IP addresses or domains, or file hashes associated with cyber threats. Threat intelligence-based detection supports other detection methods, enriching security alerts with contextual information and enabling proactive threat hunting.

How to Detect Security Threats

What is threat detection technology and how does it ensure that vulnerabilities and attacks are mitigated? Because SaaS threat detection is a systematic, repeatable, verifiable process, it reliably mitigates security challenges in an organizational IT environment.

Here are some specific ways that cloud threat detection works to detect both known and novel threats:

Continuous monitoring tools and technologies. These enable continuous network, system, application, and data monitoring.

Data collection and aggregation tools. These offer visibility into the environment and include network traffic logs, system logs, firewall logs, endpoint telemetry, user activity logs, and external threat intelligence feeds.

Varied cloud security threat detection techniques. Approaches should detect both known and previously unseen attacks, using signature-based detection, anomaly detection, behavior analysis, machine learning, and threat intelligence-based detection.

Alert generation. When security tools detect security threats, they generate alerts or notifications to alert incident response teams. These alerts typically include information about the detected event, its severity, relevant context, and recommended response actions.

Alert triage and investigation. Security analysts conduct forensic analysis, correlate related events, and assess the potential risk detected threats pose to organizational security posture.

Incident response process. Confirmed security incidents demand a controlled and documented response that can contain and eradicate threats. This may involve implementing security controls to isolate affected systems, applying patches or updates to remediate vulnerabilities, blocking malicious activities, and restoring affected data from backups.

Threat Detection in Cyber Security

Cyber security threat detection is similar to the concepts discussed above, but focuses specifically on responding to security in the digital realm.

Cybersecurity threat detection differs from and aligns with the broader threat detection context in a few ways:

Digital context. Cyber threat detection specifically addresses digital environments.

Cyberattacks. This includes any type of unauthorized attempt to gain access or compromise of the confidentiality, integrity, or availability of digital assets.

Digital data. The ability to detect cyber threats relies on data from various sources, such as user activity, application, and network traffic logs; endpoint telemetry; and external threat intelligence feeds.

Detection techniques. Cyber threat detection employs a combination of detection techniques, as described above.

Automated analysis. Cyber security operations often include automated analysis of large volumes of data using advanced analytics and machine learning algorithms that rapidly process digital data to identify malicious activity and generate notifications to prompt further action.

Human expertise. Human experts know how to detect cyber threats and remain essential for interpreting alerts, assessing the severity and impact of detected threats, and making informed decisions about response actions.

Integration with incident response. Detected threats trigger these workflows, which mitigate the impact of security breaches and prevent future incidents.

Best Practices for Detecting and Mitigating Advanced Persistent Threats

A comprehensive and multi-layered approach for how to get rid of potential threats detected includes a number of best practices:

Advanced detection techniques. Advanced strategies such as anomaly detection and sandboxing can identify sophisticated advanced persistent threat activity that may evade traditional security controls.

Continuous monitoring solutions. These monitor network traffic, endpoints, user behavior, and logs in real-time. Intrusion detection systems (IDS), EDR solutions, and security information and event management (SIEM) platforms can all help achieve this goal.

Network segmentation. This restricts lateral movement, limits the impact of threats, and prevents attackers from moving freely within the network. Access controls and least privilege principles further minimize the attack surface and contain threats within isolated network segments.

User awareness training. Educate teams about threats and common attack vectors.

Strengthen endpoint security controls. Deploy advanced endpoint protection solutions, implement application whitelisting, patch systems regularly, and use host-based intrusion prevention systems (HIPS) to detect and block advanced persistent threat-related activities on endpoints.

Incident response preparedness. Develop and test response plans with clearly defined roles and responsibilities, and conduct tabletop exercises to ensure teams are ready to respond effectively to attacks.

Maintain good security hygiene. Keep software and firmware up to date, and conduct regular vulnerability assessments and penetration testing.

Collaboration and information sharing. Exchange threat intelligence, share insights about advanced persistent threat campaigns with relevant industry peers, agencies, and other stakeholders.

Threat Detection Challenges

By any threat detection definition, the process comes with its own set of challenges:

Advanced and evolving threat landscape. Attackers change tactics regularly to evade detection.

Volume and complexity of data. Analyzing massive amounts of data from diverse sources in real-time can be complex and challenging.

False positives and alert fatigue. Distinguishing genuine security threats from noise in notifications is time-consuming and resource-intensive.

Limited visibility and context. Incomplete visibility into the environment caused by blind spots, decentralized infrastructure, legacy systems, or shadow IT makes detecting and responding to security threats effectively challenging. Furthermore, security teams may lack necessary context to understand the significance of events they do detect.

Insider threats and privileged access. Detecting unauthorized access by privileged users requires monitoring user behavior, enforcing least privilege principles, and implementing robust access controls.

Widespread use of encryption and encrypted traffic. Use of encryption can complicate SaaS threat detection because encrypted traffic may conceal malicious activities and signs of compromise.

Resource constraints. Limited budgets or a lack of trained security talent can hinder effective threat detection and impede the ability to respond to security threats promptly.

Complexity of IT environments. It is challenging to manage and secure on-premises infrastructure, cloud services, mobile devices, Internet of Things (IoT) devices, and third-party integrations while ensuring effective SaaS threat detection across all platforms.

Compliance and regulatory requirements. Mandates such as GDPR, HIPAA, PCI DSS, and industry-specific regulations impose challenging threat detection, incident response, and data protection requirements, especially for organizations operating across jurisdictions.

Integration and interoperability. Security tools and technologies often operate in silos, fragmenting threat detection and response. Integrating disparate solutions and achieving interoperability requires significant effort.

How to Build a Threat Detection Strategy

Take a systematic approach to building an effective SaaS threat detection strategy that considers the organization’s unique risk profile, business objectives, IT infrastructure, and resources:

Assess assets and risks. Identify and prioritize sensitive data, intellectual property, IT infrastructure, and key business processes. Conduct a comprehensive risk assessment to understand potential threats and vulnerabilities that could impact critical assets.

Define objectives and requirements. Base the SaaS threat detection strategy on organizational risk tolerance, compliance obligations, and security goals.

Identify threat detection requirements. Based on the risk assessment and organizational objectives, identify specific threat detection requirements and determine the types of threats to prioritize detection research..

Select appropriate technologies. Evaluate and choose the technologies and tools that support the chosen threat detection objectives.

Implement multi-layered defense. Combine preventive, detective, and corrective security controls to protect against a wide range of threats. Layer security controls at the network, endpoint, application, and data levels to provide comprehensive coverage against cyber attacks.

Establish incident response procedures. Develop and document an incident response plan and procedures to guide the organizational posture for detected threats.

Integrate security controls. This enables seamless collaboration and information sharing between different security tools and platforms.

Automate where possible. Automate repetitive tasks, such as alert triage, enrichment, and response actions.

Threat Detection and Response Tools

A number of threat detection tools identify, analyze, and respond to malicious activities within an organization’s IT environment in real-time. Examples of threat detection methods and response tools include:

  • Data collection tools aggregate information from network traffic, system logs, endpoint telemetry, user activity logs, and external threat intelligence feeds, and other sources within the organization’s IT infrastructure.
  • Event correlation threat detection solutions identify patterns, anomalies, and potential indicators of compromise within data.
  • Threat detection techniques, such as signature-based detection, anomaly detection, behavior analysis, machine learning, and threat intelligence-based detection, identify known and previously unseen or sophisticated attacks.
  • Various tools generate alerts or notifications—typically containing information about the detected event, its severity, relevant context, and recommended response actions—based on threat detection rules.
  • Threat intelligence integration tools integrate with feeds to enrich the analysis of detected threats and enhance the accuracy of threat detection.

Examples of Threat Detection Companies

There are numerous threat detection service companies that offer a wide range of products and services, including threat intelligence, network security, EDR, user behavior analytics, and more. Here are some examples of threat detection systems and related services and tools:

  • SIEM threat detection platforms such as IBM QRadar collect, correlate, and analyze security event data from across the organization’s IT infrastructure.

  • IDS like Cisco Firepower analyze network traffic for signs of malicious activity and known attack signatures to detect and block threats.

  • Tools such as Exabeam analyze user behavior analytics to detect insider threats, account compromises, and other anomalous activity.

  • Threat intelligence platforms such as ThreatConnect aggregate, analyze, and disseminate threat intelligence to enhance threat detection and response capabilities.

  • Deception technologies such as TrapX Security deploy decoys and lures to deceive attackers and gather intelligence about their tactics.

  • CrowdStrike offers an AI-powered cloud-native endpoint security platform with threat detection, endpoint protection, and incident response capabilities.

  • Palo Alto Networks offers network security, cloud security, and endpoint protection plus advanced threat detection with the Cortex XDR platform across endpoints, networks, and clouds.

Does AppOmni Offer a Threat Detection Solution?

Yes. AppOmni’s SaaS Security Platform normalizes and analyzes SaaS telemetry data from logs to activities, enabling your team to actively identify, investigate, and mitigate attacks and breaches.

With AppOmni, the team has comprehensive visibility into the activities and events occurring within SaaS applications and can identify potential security incidents, misconfigurations, unauthorized access attempts, and anomalous activities. AppOmni streamlines the collection and analysis of event logs from various SaaS applications and enables security teams to focus more on strategic security tasks rather than dealing with complex data integration and normalization processes.

Use AppOmni’s SaaS threat detection solution to identify and investigate unusual user behavior, unauthorized access attempts or data exfiltration with clear triage guidance and insights. Ensure that app owners take the right remedial actions—even if they don’t have extensive security expertise.

Learn more about AppOmni and threat detection here.