In the post-COVID-19 era, enterprises face an increasingly complex security landscape as they adopt hybrid environments, complex network architectures, and multicloud infrastructures. With over 72% of organizations now operating in multicloud environments, maintaining visibility and contextual understanding to mitigate sophisticated threats has become a significant challenge for security professionals. The rise of AI-enhanced attack strategies by malicious actors further complicates efforts, making it imperative for organizations to adopt effective practices to secure their digital assets and prevent attackers from exploiting security loopholes and cloud misconfigurations. Addressing these challenges calls for a multifaceted approach that leverages advanced technologies, robust strategies, and continuous vigilance to ensure cloud security in this evolving landscape.
Reduce the Organization’s Cloud Attack Surface
Reducing the attack surface of an organization’s cloud infrastructure does not necessarily mean decreasing the number of cloud applications in use. Instead, the focus should be on integrating artificial intelligence (AI) into security strategies, enabling security operations centers (SOCs) to counter AI-based attacks from malicious actors more effectively. AI-based behavior profiling can automate workflows, mitigate attacks, and remediate successful breaches, thereby significantly reducing the attack surface. By utilizing AI in this manner, organizations can enhance their ability to detect and respond to threats more efficiently, ultimately fortifying their defense mechanisms against cyber threats.
AI tools play a crucial role in identifying and addressing vulnerabilities long before they are exploited by attackers. By continuously monitoring and analyzing user behavior, AI can detect anomalies that may indicate potential security threats. This proactive approach allows organizations to stay one step ahead of attackers and minimize the risk of successful breaches. Leveraging AI for continuous monitoring and automated responses can greatly enhance an organization’s security posture, providing an advanced layer of defense against increasingly sophisticated cyber threats.
Utilize AI for Predictive Remediation
AI tools are instrumental in expediting threat detection, investigation, and incident response, making them indispensable in modern cybersecurity strategies. Machine learning-based user and entity behavior analytics (UEBA) tools, for instance, excel at identifying anomalous behaviors across the network. These tools facilitate rapid investigation of potential threats and automate responses to proactively mitigate and remediate attacks. The overarching goal for security professionals is to identify vulnerabilities before they are exploited, and AI tools are pivotal in achieving this objective by providing predictive insights and enabling preemptive action.
Predictive remediation involves utilizing AI to anticipate potential threats and take preemptive measures to neutralize them. By analyzing patterns and trends in network activity, AI can predict where and how attacks might occur, allowing security teams to implement countermeasures in advance. This approach enhances security by reducing the time and resources required to respond to incidents. By leveraging AI for predictive remediation, organizations can anticipate and thwart attacks before they cause significant damage, ensuring a more robust and resilient security posture.
Use Identity Mapping to Bolster Cloud Security Threat Detection
As enterprises increasingly transition to cloud-based operations, identity security has gained precedence over traditional endpoint security. Security professionals now focus more on identifying anomalous user behavior rather than the specifics of how, where, or why such behaviors occur. By mapping cloud activities to specific users, security teams can derive contextual data regarding resource access and application usage, building a comprehensive profile of each user. This identity mapping technique is crucial for detecting unusual activities that may indicate a security threat, such as a user suddenly accessing a sensitive resource they have never used before.
Monitoring and analyzing user behavior through identity mapping allows security teams to quickly identify and respond to potential threats. For example, if a user’s activity deviates from their typical patterns, it could suggest a compromised account. By rapidly identifying and investigating such anomalies, organizations can mitigate risks and prevent unauthorized access to sensitive data. Identity mapping thus provides a targeted approach to threat detection, enhancing cloud security by enabling early identification and swift response to potential breaches.
Rely on a Centralized Platform to Investigate Threats Across a Multicloud Environment
Utilizing centralized platforms allows security personnel to assess the potential impacts of threats across distributed or multitenant surfaces more efficiently. These platforms offer a unified response center that automates workflows by orchestrating with various cloud applications, reducing the mean time to resolve (MTTR) incidents and threats. By consolidating threat data from multiple sources, centralized platforms present a holistic view of the security landscape, facilitating more coordinated and comprehensive threat investigations across a multicloud environment.
A centralized approach simplifies the management of security policies and procedures, enabling security teams to use a single platform for monitoring, analyzing, and responding to threats. This consolidation improves efficiency by reducing the complexity of dealing with disparate systems and tools. Moreover, it ensures a more consistent and coordinated response to security incidents, enhancing the overall effectiveness of an organization’s security efforts. Centralized platforms thus play a crucial role in streamlining threat investigation processes, enabling faster and more accurate threat detection and response.
Correlate Network Events with Cloud Activities
Analyzing data from both network and cloud services allows security professionals to recognize patterns, relationships, and potential threats effectively. Organizations must carefully design, test, and implement correlation rules tailored to their cloud security data. By correlating network events with cloud activities, security teams can identify anomalous traffic, unusual account usage, or unauthorized access to cloud storage. For instance, a security operations center (SOC) professional investigating potential data exfiltration from a cloud CRM tool would correlate logs from the CRM with logs from email or team communication tools to uncover compromised accounts or data exfiltration attempts.
Effective correlation requires a deep understanding of the organization’s network and cloud environments. Security teams must continuously update and refine their correlation rules to keep pace with evolving threats. By maintaining and improving correlation rules, organizations can enhance their ability to detect and respond to security incidents in real-time. This comprehensive approach ensures that potential threats are identified and addressed promptly, bolstering the overall security of the cloud infrastructure.
Eliminate Shadow IT and Regularly Conduct Cloud Security Risk Assessments
The unsanctioned use of applications, known as shadow IT, poses significant security risks and has worsened since the pandemic began. Shadow IT can lead to vulnerabilities and potential threats as unauthorized applications may lack appropriate security measures. To mitigate these risks, security personnel should frequently perform cloud security risk assessments and audits. Adopting a bottom-up approach, starting with granular components and progressing to the overall network, helps Chief Information Security Officers (CISOs) gain a comprehensive understanding of the security posture and identify potential weaknesses.
Regular risk assessments are essential for identifying and addressing vulnerabilities before they can be exploited by attackers. By continuously monitoring and evaluating the security of their cloud environments, organizations can stay ahead of emerging threats and ensure the integrity of their digital assets. This proactive approach to risk management helps organizations maintain a robust security posture, minimizing the likelihood of successful cyberattacks and safeguarding critical information.
Have a Well-Defined Incident Response Plan in Place
AI tools play a crucial role in speeding up threat detection, investigation, and response, making them vital in today’s cybersecurity. Machine learning-based user and entity behavior analytics (UEBA) tools are particularly adept at spotting unusual activities in the network. These tools enable swift investigations of potential threats and automate responses to mitigate and neutralize attacks proactively. Security experts aim to identify vulnerabilities before they can be exploited, and AI tools are essential in this process by offering predictive insights and facilitating preemptive actions.
Predictive remediation, a strategy using AI, helps foresee potential threats and take early measures to neutralize them. By examining patterns and trends in network activities, AI can forecast where and how attacks might occur, enabling security teams to establish countermeasures beforehand. This method improves security by reducing the time and effort needed to react to incidents. By utilizing AI for predictive remediation, organizations can anticipate and prevent attacks before they inflict substantial damage, ensuring a stronger and more resilient security stance.