How to Automate Your Enterprise Cybersecurity with an AI-Powered SOC

Cybersecurity professionals need help keeping up with the increasing number of alerts from enterprise security. According to IT Security Wire, a 2020 survey found that four out of five SOC analysts' volume of security alerts increased by as much as 50 percent over the previous year.

The survey found that 70% of respondents said they had to manually investigate more than ten security alerts daily. This is up by 25% compared to two years ago. It's also a cause for concern, as only 40% said they have enough time to analyse and remedy actual security incidents of concern.

A security operations approach based on alerts wastes valuable resources and time chasing false positives. This time could be better spent addressing important security events or other high-priority tasks.

The role of AI in cybersecurity

Artificial intelligence (AI) and machine learning (ML) are revolutionising the field of cybersecurity. AI-powered systems can analyse vast amounts of data in real time, identify patterns, and detect anomalies that may indicate a cyber threat. They can also automate responses and adapt to evolving threats without human intervention.

AI in cybersecurity offers the following capabilities:

• Anomaly Detection: AI algorithms can detect unusual behaviour patterns within a network or system, which may indicate a breach or unauthorised access.

• Predictive Analysis: AI can analyse historical data to predict future threats and vulnerabilities, allowing organisations to proactively address security weaknesses.

• Automation: AI can automate routine security tasks, such as patch management and threat response, reducing the burden on human analysts.

• Incident Response: AI-powered systems can rapidly identify and respond to security incidents, minimising damage and downtime.

Embracing AI and its Benefits

Many organisations use artificial intelligence and machine learning tools to automate triage and investigation efforts. PwC reported that over half of U.S. executives had accelerated AI adoption following the events of 2020. Even more (86%) stated that AI will be a mainstream technology by 2021.

These findings demonstrate AI's benefits to an organisation's security efforts. Booz Allen stated that organisations could also use these technologies to detect nuanced attacks sooner than they could with manual investigation.

By automating security operations, organisations can improve their response to incidents and reduce the risk of a potential threat.

AI allows security teams to reduce the noise created by the constant stream of alerts. This means that security professionals can spend less time sorting through alerts or chasing false positives and more time improving the organisation's security posture.

Artificial Intelligence as Force Multipliers

AI technology can also help close the cybersecurity skills gap. Organisations need people to run a robust security operation around the clock. The initial inertia can make implementing AI more challenging, but the benefits outweigh this cost.

AI can analyse large data sets accurately and quickly, allowing it to detect events of concern in a way that manual analysis cannot. This is a great way to automate the detection of events that require human analysis and relieve security teams from sorting out the network's noise.

AI is not a silver bullet. Humans will need to work with AI for the foreseeable future. However, AI can increase the effectiveness of every member of the security team.

PerfectionGeeks Delivers Advanced AI

Techniques like behavioural analytics, which leverage indicators of behaviour (IOBs), can provide a deeper understanding of how attackers conduct their campaigns. This operation-centric method is superior to other methods for detecting attacks, especially highly targeted attacks that use tools and tactics never seen before.

Finding behavioural signals allows defenders to view the entire attack from the root cause across all impacted users and devices. Even the most experienced human analysts cannot efficiently and quickly query all available telemetry to uncover meaningful attack indicators.

Artificial intelligence (AI) and machine learning can analyse and correlate data automatically for up to millions of events every second. Analysts can spend less time manually querying data and more time implementing the insights generated by AI.

The Cybereason Defence Platform uses multiple layers of AI to identify digital threats, including never-before-seen malware strains, ransomware attacks, and complex attack sequences. These capabilities enable security teams to quickly remediate known and unknown threats, regardless of their location in an organisation's environment.

This visibility allows security teams to react to an incident before it becomes a severe security issue and introduce measures that will increase the burden placed on attackers. The Cybereason Defence Platform has been designed from the ground up for scale, artificial intelligence, and machine learning on all levels, including the machine and enterprise levels.

In case you missed it, Cybereason, Google Cloud, and Microsoft recently formed a strategic alliance to develop a joint solution to support our mission of reversing the adversary's advantage. This partnership is a key one that delivers an AI-powered XDR Security Platform that can ingest petabytes of telemetry across the entire IT and security stack. It offers unparalleled speed and accuracy in preventing and detecting advanced threats to endpoints, networks, containers, and application suites.

Because their platforms cannot analyse events at scale, competing offerings like those from Crowdstrike or SentinelOne cannot scan nonexecutable files and provide behavioural ransomware protection. They must use "smart filters" to eliminate critical telemetry required to detect and stop an attack at its earliest stages. Eliminating telemetry from the analysis hinders any ability to apply AI for automated detection and response.

Cybereason XDR powered by Chronicle combines the industry-leading Cybereason Defence Platform and its patented MalOp (tm) (malicious operation) engine, which analyses over 23 trillion security-related events every week, with Google Cloud's cybersecurity analysis engine that ingests petabytes worth of telemetry across the entire IT infrastructure. Cybereason's and Google's combined capabilities ensure that no telemetry will be filtered, allowing the AI predictive analysis engine to identify and remediate attacks earlier.

Steps to Automate Enterprise Cybersecurity with an AI-Powered SOC

Implementing an AI-powered SOC involves several key steps:

• Assessment and planning

  Assess Current Capabilities: Begin by assessing your organisation's existing cybersecurity capabilities and identifying areas where AI can provide the most value.

  Define Objectives: Clearly define your objectives and goals for implementing AI in your SOC. What specific security challenges are you trying to address

• Selecting the Right AI Tools

  Vendor Selection: Research and choose AI security vendors or platforms that align with your organisation's needs and goals.

  Customization: Customise AI solutions to meet your specific requirements and integrate them with your existing security infrastructure.

• Integration and Training

  Integration: Integrate AI-powered solutions seamlessly with your existing security tools, such as SIEM (Security Information and Event Management) systems.

  Training: Train SOC analysts and staff to work effectively with AI systems. Ensure they understand how to interpret AI-generated alerts and reports.

• Continuous monitoring and optimization

  Continuous Monitoring: Regularly monitor the performance of AI-powered systems and fine-tune them as needed to reduce false positives and improve accuracy.

  Threat Intelligence: Incorporate threat intelligence feeds to keep AI systems updated with the latest threat information.

  Adaptive Response: Implement adaptive response strategies that allow AI systems to autonomously respond to threats while keeping human analysts in the loop for critical decisions.

Real-World Applications

AI-powered SOCs are already making a significant impact on cybersecurity across various industries. Examples of real-world applications include:

• Threat Detection: AI systems are used to detect advanced threats, including zero-day vulnerabilities and sophisticated malware.

• Phishing Detection: AI-powered email security solutions can identify and block phishing emails, protecting organisations from email-based attacks.

• Endpoint Security: AI-driven endpoint detection and response (EDR) solutions can identify and respond to threats on individual devices.

• Network Security: AI systems can continuously monitor network traffic for anomalies and intrusions, providing early warning of potential attacks.

Conclusion

In an era of increasingly sophisticated cyber threats, automating your enterprise cybersecurity with an AI-powered SOC is not just an option; it's a necessity. AI systems can provide real-time threat detection, rapid incident response, and improved overall security posture. However, successful implementation requires careful planning, integration, and ongoing monitoring to maximise the benefits while addressing challenges and ensuring compliance with data privacy regulations. By embracing AI-powered cybersecurity solutions, organisations can better protect their digital assets and stay one step ahead of cyber adversaries.