Adopting a Risk-Based Correlation Model for IT and Cybersecurity.

In the hyperconnected digital ecosystem, organizations face an unprecedented array of cyber threats that continue to evolve in sophistication, frequency, and impact. Traditional cybersecurity approaches that rely solely on defensive technologies and compliance-based strategies have proven insufficient against the modern threat landscape. The sheer volume of security alerts, the complexity of IT infrastructures spanning on-premises and cloud environments, and the growing attack surface created by remote work and IoT devices have overwhelmed security operations centers worldwide. Organizations are drowning in data but starving for actionable intelligence. This disconnect highlights the critical need for a paradigm shift in how we approach cybersecurity - moving from reactive, siloed security practices to proactive, intelligence-driven risk management. A risk-based correlation model represents this fundamental shift by contextualizing security events within the broader risk landscape of an organization. Rather than treating each security alert as an isolated incident, this approach correlates data across multiple security domains, considers business context, asset criticality, threat intelligence, and vulnerability data to identify the threats that pose the greatest risk to business objectives. By adopting a risk-based correlation model, organizations can transcend the limitations of traditional security monitoring to develop a comprehensive understanding of their cybersecurity posture. This strategic approach enables security teams to prioritize responses based on business impact, allocate resources more effectively, and communicate security risks in terms that resonate with executive leadership. As cybersecurity continues to evolve from a technical discipline to a strategic business function, the ability to correlate security events with business risk becomes not merely advantageous but essential for organizational resilience in the digital age. This blog explores the critical components, implementation strategies, and benefits of adopting a risk-based correlation model for IT and cybersecurity.
The Limitations of Traditional Security Monitoring Approaches
Traditional security monitoring approaches have long been the cornerstone of organizational cybersecurity strategies, relying heavily on signature-based detection, perimeter defense, and rule-based alerts. However, these conventional methodologies have reached their functional limits in today's complex threat landscape. The fundamental problem lies in their inherent reactive nature – they typically detect known threats using predefined signatures and struggle to identify novel attack vectors or sophisticated adversaries employing evasion techniques. Security information and event management (SIEM) systems, while valuable for log aggregation, frequently generate overwhelming volumes of alerts without adequate context or prioritization, leading to the pervasive problem of alert fatigue among security teams. Studies consistently show that security analysts investigate less than 50% of daily alerts, with false positives consuming precious time and resources while potentially allowing genuine threats to go undetected. The siloed approach to security monitoring further exacerbates these challenges, as discrete security tools operate independently without sharing critical context. Network monitoring tools may detect suspicious traffic but lack visibility into endpoint behaviors, while vulnerability scanners identify potential weaknesses without considering real-world exploitability or business impact. This fragmentation creates dangerous blind spots as modern attack chains traverse multiple vectors and exploit interconnected vulnerabilities. The lack of business context represents another significant limitation, as traditional security monitoring rarely incorporates asset criticality, data sensitivity, or business process importance into its alerting mechanisms. A minor vulnerability in a critical customer database deserves more urgent attention than a severe vulnerability in a non-production test environment, yet traditional monitoring systems typically fail to make this crucial distinction. Additionally, these conventional approaches struggle with the expanded attack surface created by cloud environments, remote work, mobile devices, and IoT implementations – all of which generate massive data volumes that overwhelm traditional correlation rules. Finally, the compliance-oriented mindset that often drives traditional security monitoring focuses more on satisfying regulatory requirements than addressing actual security risks, creating a dangerous false sense of security for organizations that are "compliant" but not necessarily secure. These fundamental limitations underscore the urgent need for a more sophisticated, risk-aware approach to security monitoring that can provide meaningful context, intelligent prioritization, and business-aligned security insights.
Understanding Risk-Based Correlation: Fundamental Principles and Framework
Risk-based correlation represents a sophisticated evolution in cybersecurity methodology that transcends traditional alert-centric monitoring by contextualizing security data within a comprehensive risk framework. At its core, this approach is built upon the fundamental principle that not all security events carry equal importance – their significance is determined by the potential business impact, the specific assets targeted, the threat actors involved, and the vulnerabilities exploited. This model employs advanced analytics to establish meaningful connections between seemingly disparate security events, revealing attack patterns and threat narratives that would otherwise remain obscured in isolation. The framework begins with comprehensive data collection from across the security ecosystem – including endpoint detection and response (EDR) systems, network monitoring tools, cloud security platforms, identity management solutions, and application security controls. This multi-dimensional data is then enriched with critical contextual elements: asset inventory and criticality ratings that identify the business value of affected systems; vulnerability data that reveals potential weaknesses; threat intelligence that provides insights into attacker methodologies and motivations; and business context that connects security events to operational processes and objectives. Risk-based correlation employs both rule-based logic and machine learning algorithms to identify meaningful relationships between these diverse data points. Rules establish baseline correlations for known attack patterns, while machine learning identifies subtle, complex relationships that evade predefined logic. The correlation engine evaluates security events against risk-based criteria such as the potential impact on business operations, data confidentiality, system integrity, and service availability. This evaluation generates a dynamic risk score that quantifies the overall threat level posed by correlated events, enabling prioritization based on business risk rather than technical severity alone. The model is inherently adaptive, continuously learning from new data, analyst feedback, and evolving threat landscapes to refine its correlation algorithms and risk assessments. This adaptability is crucial in countering sophisticated adversaries who regularly modify their tactics, techniques, and procedures. Finally, the framework emphasizes actionable intelligence over mere alerting, providing security teams with contextualized insights that facilitate informed decision-making. Rather than simply notifying analysts that an event has occurred, risk-based correlation explains why the event matters, how it relates to other security incidents, which business assets are at risk, and what actions should be prioritized to mitigate the threat effectively. By establishing this comprehensive risk-based correlation framework, organizations transform security monitoring from a reactive technical function into a strategic business enabler that aligns security operations with enterprise risk management.
Asset Inventory and Criticality: The Foundation of Risk-Based Correlation
The cornerstone of any effective risk-based correlation model lies in developing a comprehensive, accurate, and continuously updated asset inventory coupled with meaningful criticality assessments. Without knowing what assets exist in your environment and understanding their relative importance to business operations, security teams operate in a contextual vacuum, unable to differentiate between alerts that threaten mission-critical systems and those affecting non-essential resources. A robust asset inventory must transcend traditional IT asset management by capturing not only physical and virtual infrastructure components but also cloud resources, data repositories, applications, and the complex interdependencies between these elements. This inventory should document essential metadata including ownership, location, operating systems, installed software, configurations, patch levels, and authentication mechanisms – all critical elements for accurate risk assessment. Organizations should implement automated discovery tools that continuously scan the environment to identify new assets, detect changes to existing assets, and remove decommissioned systems from active monitoring. This automation is essential given the dynamic nature of modern IT environments where cloud resources can be provisioned or decommissioned in minutes, making manual inventory processes woefully inadequate. Beyond mere identification, each asset must be evaluated for its criticality to business operations through a formalized assessment process that considers multiple dimensions. Data sensitivity ratings should classify information based on regulatory requirements, intellectual property value, and potential impact if compromised. Business process alignment should identify which core business functions depend on specific assets and the operational impact of their disruption. Recovery time objectives should establish how quickly systems need to be restored to avoid significant business consequences. Interconnection mapping should document dependencies between systems, as seemingly low-value assets may provide critical access paths to high-value targets. Organizations should develop a tiered classification system – typically ranging from mission-critical to non-essential – with clear criteria for each tier, and regularly validate these classifications with business stakeholders to ensure alignment with current operations. The asset criticality assessment must be dynamic, reflecting changing business priorities; a system that was once peripheral may become essential during specific business cycles, such as financial systems during quarter-end reporting periods. This contextual understanding enables security teams to adjust monitoring sensitivity and response prioritization accordingly. Integration of the asset inventory and criticality ratings into the security monitoring ecosystem is equally crucial, enabling correlation engines to automatically elevate alerts affecting high-criticality assets while appropriately prioritizing incidents involving less essential systems. By establishing this foundation of comprehensive asset knowledge and business-aligned criticality assessments, organizations create the essential context required for meaningful risk-based correlation, transforming security monitoring from indiscriminate alerting to business-focused risk management.
Threat Intelligence Integration: Contextualizing Security Events with Adversary Insights
Threat intelligence integration serves as a critical dimension in a mature risk-based correlation model, providing essential context about the adversaries, their tactics, techniques, and procedures (TTPs), and the broader threat landscape that contextualizes security events within your environment. Without this intelligence layer, security teams operate with significant blind spots, unable to differentiate between random scanning activities and targeted campaigns orchestrated by sophisticated threat actors specifically pursuing your organization or industry. Effective threat intelligence integration begins with a multi-tiered approach to intelligence collection that encompasses tactical, operational, and strategic levels. Tactical intelligence includes technical indicators such as malicious IP addresses, domains, file hashes, and network signatures that enable direct correlation with security events in your environment. Operational intelligence focuses on understanding adversary methodologies, providing context about attack patterns, exploitation techniques, and lateral movement strategies that help security teams recognize coordinated campaigns that might appear as unrelated events in isolation. Strategic intelligence delivers broader insights into threat actor motivations, industry-specific targeting patterns, and emerging threats on the horizon, enabling proactive defense adjustments before attacks materialize. Organizations should implement bidirectional integration between threat intelligence platforms and security monitoring tools, enabling automated enrichment of security alerts with relevant intelligence while simultaneously allowing newly detected indicators from internal security events to enhance the intelligence repository. This continuous feedback loop creates an ever-evolving knowledge base that improves detection efficacy over time. The correlation engine should leverage this integrated intelligence to perform several critical functions: attribution analysis that links observed activities to known threat actors or campaigns; similarity analysis that identifies connections between current events and historical attacks; intent analysis that assesses whether observed behaviors indicate targeted attacks or opportunistic activities; and predictive analysis that anticipates likely next steps based on established attack patterns. Advanced risk-based correlation models employ machine learning algorithms to identify subtle correlations between internal security events and external threat intelligence, revealing hidden connections that would evade manual analysis or static rules. These algorithms continuously refine their correlation logic through feedback mechanisms, improving detection accuracy and reducing false positives over time. Organizations should also implement threat intelligence relevancy scoring that automatically evaluates intelligence feeds against their specific technology stack, industry sector, geographical footprint, and current security posture, ensuring that only applicable intelligence impacts correlation outcomes and risk scores. This targeted approach prevents intelligence overload while ensuring relevant threats receive appropriate attention. Finally, the integration should include temporal analysis capabilities that consider the recency and persistence of threat intelligence, appropriately weighting current, active campaigns higher than historical threats with declining relevance. By seamlessly integrating multi-dimensional threat intelligence into the correlation model, organizations transform security monitoring from isolated event detection to contextual threat awareness, enabling security teams to understand not just what is happening in their environment, but who might be behind it, why they are being targeted, and what the adversary's ultimate objectives might be.
Vulnerability Intelligence: Prioritizing Exposures Based on Exploitability and Impact
Vulnerability intelligence represents a pivotal component in a comprehensive risk-based correlation model, transforming traditional vulnerability management from an overwhelming catalog of potential weaknesses into a strategic framework for prioritizing actual risk based on exploitability and business impact. The conventional approach to vulnerability management, which often relies primarily on CVSS scores, has proven inadequate in helping security teams focus their remediation efforts effectively. Organizations regularly face thousands of vulnerabilities across their infrastructure, making complete remediation impossible and prioritization essential. Vulnerability intelligence elevates this prioritization process by enriching raw vulnerability data with crucial contextual factors that determine actual risk. Exploitability assessment examines whether vulnerabilities are being actively exploited in the wild, have public exploit code available, or require sophisticated techniques accessible only to advanced adversaries. Threat actor intelligence considers which threat groups are known to target specific vulnerabilities and whether these actors pose a relevant threat to your organization or industry. Environmental context evaluates whether vulnerable assets are exposed to the internet, protected by compensating controls, or isolated in segmented networks. Business impact analysis assesses the potential operational, financial, and reputational consequences if vulnerable systems were successfully compromised. A sophisticated risk-based correlation model integrates vulnerability intelligence directly into the security monitoring workflow, automatically correlating detected security events with known vulnerabilities in the affected systems. This integration enables the correlation engine to significantly elevate the risk score when security events target systems with exploitable vulnerabilities, particularly when those vulnerabilities align with the observed attack pattern. For example, a seemingly low-priority port scan would receive heightened attention if directed at systems with recently disclosed, easily exploitable vulnerabilities that match the scanning pattern. Organizations should implement continuous vulnerability assessment processes that regularly scan their environment and immediately notify security monitoring systems of newly discovered vulnerabilities, ensuring correlation rules have access to current vulnerability data. This near-real-time vulnerability intelligence is particularly crucial given the rapidly shrinking window between vulnerability disclosure and active exploitation, which has decreased from months to days or even hours for high-profile vulnerabilities. Advanced correlation models also incorporate vulnerability prediction capabilities that leverage machine learning to identify systems most likely to contain undiscovered vulnerabilities based on factors such as patch history, configuration complexity, and similarity to previously vulnerable systems. This predictive approach enables proactive monitoring of potentially vulnerable systems even before specific vulnerabilities are identified. Finally, the vulnerability intelligence component should include automated risk reassessment triggers that instantly recalculate risk scores across the environment when high-impact vulnerabilities are disclosed or when exploit code becomes publicly available. This dynamic approach ensures that correlation rules remain aligned with the evolving vulnerability landscape. By integrating comprehensive vulnerability intelligence into the risk-based correlation model, organizations transform vulnerability management from a disconnected compliance activity into an integral component of their threat detection and response capabilities, focusing security resources on the vulnerabilities that pose the greatest actual risk to business operations.
Behavioral Analytics: Detecting Abnormal Activity Patterns Through Baseline Profiling
Behavioral analytics represents a sophisticated advancement in the risk-based correlation model, moving beyond signature-based detection to identify subtle deviations from established patterns that may indicate compromise even when no known indicators of compromise are present. This approach is particularly valuable for detecting advanced persistent threats, insider threats, and novel attack methodologies that evade traditional detection mechanisms. At its foundation, behavioral analytics establishes comprehensive baselines across multiple dimensions of organizational activity, creating a multi-faceted "normal" against which anomalies can be identified. User behavior baselines profile typical patterns of authentication, resource access, working hours, geographic locations, and system interactions for each user or user group, enabling detection of account takeovers or insider threat activities. Entity behavior baselines monitor typical communication patterns, data transfer volumes, and process activities for servers, endpoints, and network devices, revealing lateral movement or data exfiltration attempts. Application behavior baselines track standard API call patterns, database query frequencies, and resource utilization, highlighting potential application attacks or abuse. Network behavior baselines establish normal traffic flows, protocol usage, and connection patterns, identifying command and control communications or reconnaissance activities. These baselines must be dynamic rather than static, automatically adjusting to accommodate legitimate changes in business operations, such as new applications, organizational restructuring, or seasonal business patterns. Advanced risk-based correlation models employ sophisticated machine learning algorithms that continuously refine these baselines based on observed patterns, reducing false positives while maintaining detection sensitivity. The behavioral analytics component correlates anomalies across multiple dimensions to distinguish between isolated deviations and coordinated malicious activity. A user accessing an unusual server might generate a low-priority alert, but when that access is combined with anomalous data transfers, off-hours activity, and communication with unknown external domains, the correlated behaviors reveal a compelling risk narrative that warrants immediate investigation. Organizations should implement hierarchical anomaly scoring that evaluates deviations at increasing levels of aggregation – from individual actions to session-level behaviors to campaigns – assigning appropriate risk scores based on the scope and persistence of the anomalous pattern. This multi-tiered approach reduces alert fatigue from isolated anomalies while ensuring that sustained abnormal behaviors receive appropriate attention. Contextual enrichment is equally crucial, with the behavioral analytics component integrating data from identity and access management systems, HR databases, change management workflows, and business calendars to differentiate between legitimate activities and genuine threats. A user accessing sensitive systems outside normal hours might be suspicious, but that same access during a scheduled maintenance window or by a team member temporarily covering for an absent colleague represents normal operations. Organizations should also implement federated learning techniques that enable behavioral models to benefit from patterns observed across multiple organizations while preserving data privacy, significantly improving detection capabilities for emerging threats without exposing sensitive data. By incorporating sophisticated behavioral analytics into the risk-based correlation model, organizations gain the ability to detect subtle indicators of compromise that would evade traditional security monitoring, identifying malicious activity based on behavioral deviations rather than known signatures or rules, and substantially reducing the blind spots that sophisticated adversaries exploit to maintain persistence in compromised environments.
Automation and Orchestration: Streamlining Response Through Integrated Workflows
Automation and orchestration serve as the operational backbone of an effective risk-based correlation model, transforming security insights into coordinated action through integrated workflows that dramatically reduce response times, eliminate manual errors, and ensure consistent execution of security protocols. As the volume and complexity of security events continue to grow, human-centric response processes have become increasingly inadequate, with studies indicating that manual investigation and remediation typically consume hours or days for incidents that automation can address in seconds or minutes. The automation journey begins with systematic identification of repetitive, rule-based security tasks that are prime candidates for automation. Initial triage processes that gather contextual information about affected assets, query threat intelligence platforms, and check vulnerability status can be fully automated, providing analysts with comprehensive context before they begin investigation. Containment actions for well-understood threats, such as isolating compromised endpoints, blocking malicious IP addresses, or disabling compromised credentials, can be implemented through predefined playbooks that execute automatically when correlation rules identify high-confidence threats. Remediation workflows for common issues like patch deployment, configuration corrections, or access control adjustments can be triggered automatically based on correlation findings, closing security gaps before they can be exploited further. Organizations should implement a tiered automation approach that balances speed with appropriate human oversight. Fully automated responses execute without human intervention for high-confidence, low-risk scenarios like known malware containment or vulnerability patching. Semi-automated workflows present analysts with recommended actions and supporting evidence for approval before execution, appropriate for high-impact actions like system isolation or account suspension. Human-centric automation provides decision support through enriched alerts and contextual dashboards while leaving execution control entirely with analysts, suitable for complex incidents requiring nuanced investigation. The orchestration layer serves as the connective tissue between disparate security tools, enabling coordinated response across the entire security ecosystem. API-based integrations with endpoint protection platforms, network security devices, cloud security services, identity management systems, and IT service management tools create a unified response fabric that eliminates manual tool-switching and copy-paste operations that delay traditional response processes. Organizations should implement closed-loop feedback mechanisms that track the effectiveness of automated responses, capturing metrics on false positive rates, mean time to detect, mean time to respond, and incident resolution times. This performance data enables continuous refinement of automation rules, ensuring that automated responses become increasingly accurate and effective over time. Advanced orchestration platforms employ machine learning to develop adaptive playbooks that evolve based on observed effectiveness, analyst feedback, and changing threat landscapes, moving beyond static workflows to dynamic response strategies that adjust to emerging attack patterns. Finally, organizations should implement comprehensive logging and chain-of-custody documentation for all automated actions to support compliance requirements, incident postmortems, and potential legal proceedings. By integrating sophisticated automation and orchestration capabilities into the risk-based correlation model, organizations dramatically accelerate their security response processes, addressing threats at machine speed while freeing security analysts to focus on complex investigation, threat hunting, and continuous improvement of security defenses rather than repetitive operational tasks. This operational efficiency translates directly into reduced risk exposure, as the window of opportunity for attackers to achieve their objectives is substantially narrowed through immediate, consistent, and coordinated response actions.
Governance and Continuous Improvement: Sustaining the Risk-Based Correlation Model
Governance and continuous improvement represent the strategic framework that ensures a risk-based correlation model remains effective, accurate, and aligned with evolving business objectives and threat landscapes over time. Without robust governance processes, even the most sophisticated correlation models eventually degrade as environments change, threats evolve, and security teams experience personnel transitions. This sustainable approach begins with establishing formalized ownership and accountability structures for the correlation model, clearly defining roles and responsibilities across multiple stakeholders. Executive sponsorship from senior leadership, ideally the CISO or CIO, provides necessary resources and organizational alignment. A dedicated correlation engineering team maintains technical components, updates correlation rules, and implements new detection capabilities. Business unit representatives provide ongoing input regarding changing business processes and priorities. Compliance and risk management stakeholders ensure alignment with regulatory requirements and enterprise risk frameworks. Security operations analysts deliver feedback on alert quality, false positive rates, and investigation outcomes to drive continuous refinement. Organizations should implement structured review cycles that regularly evaluate the effectiveness of the correlation model across multiple dimensions. Quarterly technical reviews should assess detection coverage against the current threat landscape, evaluate false positive and false negative rates, and identify gaps in data sources or correlation logic. Bi-annual business alignment reviews should validate asset criticality ratings, update business process mappings, and ensure correlation outputs remain relevant to business risk management. Annual strategic reviews should evaluate the correlation model against the organization's security strategy, emerging technologies, and evolving regulatory requirements, setting the roadmap for major enhancements. Regular testing and validation processes are equally crucial, with routine adversary emulation exercises that simulate realistic attack scenarios to verify detection capabilities. Red team assessments should specifically target and attempt to evade correlation rules, identifying blind spots and detection gaps. Regular tabletop exercises should validate that correlation outputs effectively support incident response processes and decision-making during security incidents. Organizations should establish comprehensive metrics frameworks to objectively evaluate correlation effectiveness and drive data-informed improvements. Technical metrics should track alert volumes, false positive rates, mean time to detect, and coverage gaps. Operational metrics should measure analyst productivity, alert investigation times, and resource utilization. Business impact metrics should quantify risk reduction, security incident costs, and alignment with risk tolerance thresholds. Knowledge management practices are essential for maintaining operational continuity despite inevitable staff turnover, with detailed documentation of correlation logic, investigation procedures, and the rationale behind design decisions. Knowledge transfer sessions should regularly share insights across the security team, preventing critical expertise from becoming siloed with specific individuals. Finally, organizations should implement structured feedback loops that systematically capture insights from multiple sources to drive continuous improvement. Post-incident reviews should examine whether correlation rules effectively detected and prioritized incident indicators. Threat hunting findings should inform correlation enhancements based on newly discovered threats. Peer benchmarking should identify best practices and emerging methodologies from other organizations. By establishing these comprehensive governance and continuous improvement processes, organizations ensure that their risk-based correlation model remains a living, evolving capability rather than a static implementation that gradually diminishes in effectiveness as the security landscape changes. This sustainable approach transforms security correlation from a one-time project into an enduring operational capability that continuously adapts to provide relevant, accurate, and actionable risk insights across the evolving digital ecosystem.
Conclusion: The Strategic Imperative of Risk-Based Correlation in Modern Cybersecurity
The adoption of a risk-based correlation model represents far more than a technical enhancement to security operations—it embodies a fundamental paradigm shift in how organizations conceptualize, prioritize, and address cybersecurity challenges in today's complex digital landscape. As we have explored throughout this discussion, traditional siloed security monitoring approaches have become increasingly inadequate against sophisticated threats that exploit the interconnected nature of modern IT environments, leaving security teams overwhelmed by alert volumes while struggling to identify the threats that truly matter to business objectives. Risk-based correlation transcends these limitations by establishing a comprehensive framework that contextualizes security events within the broader business risk landscape, enabling security teams to focus their limited resources on the threats that pose the greatest potential impact to organizational objectives. The journey toward implementing this model requires substantial commitment across multiple dimensions: developing accurate asset inventories and criticality assessments that connect security events to business value; integrating multi-faceted threat intelligence that provides adversary context; incorporating vulnerability intelligence that prioritizes actual exploitation risk; deploying behavioral analytics that detect subtle anomalies indicating compromise; implementing automation and orchestration capabilities that accelerate response; and establishing governance frameworks that ensure the model's sustained effectiveness through continuous improvement. Organizations that successfully navigate this transformation achieve several strategic advantages that extend well beyond improved threat detection. Security operations become dramatically more efficient as analysts focus on genuinely significant threats rather than drowning in undifferentiated alerts. Risk management becomes more precise as security investments target the vulnerabilities and threats that pose actual business risk rather than theoretical technical concerns. Executive communication improves as security teams translate technical incidents into business risk narratives that resonate with leadership. Regulatory compliance becomes a natural byproduct of effective risk management rather than a separate, resource-intensive effort. As cyber threats continue to evolve in sophistication and impact, the strategic importance of risk-based correlation will only increase. Organizations that remain tethered to traditional, siloed security monitoring approaches will find themselves at a growing disadvantage, constrained by limited visibility, overwhelmed by alert volumes, and unable to distinguish between critical threats and background noise. Conversely, those that embrace risk-based correlation position themselves to navigate the evolving threat landscape with clarity, precision, and strategic alignment between security operations and business objectives. The implementation journey may be challenging, requiring significant investments in technology, processes, and expertise, but the alternative—continuing with security approaches fundamentally misaligned with today's threat reality—represents an increasingly untenable position. In the final analysis, risk-based correlation is not merely a security enhancement but a business imperative, enabling organizations to protect their most valuable assets, maintain operational resilience in the face of sophisticated threats, and ultimately transform cybersecurity from a technical function into a strategic business enabler aligned with enterprise objectives in our interconnected digital world. To know more about Algomox AIOps, please visit our Algomox Platform Page.