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:
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Anomaly Detection: AI algorithms can detect unusual behaviour patterns within a
network
or system, which may indicate a breach or unauthorised access.
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Predictive Analysis: AI can analyse historical data to predict future threats and
vulnerabilities, allowing organisations to proactively address security weaknesses.
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Automation: AI can automate routine security tasks, such as patch management and
threat
response, reducing the burden on human analysts.
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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:
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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
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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.
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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.
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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:
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Threat Detection: AI systems are used to detect advanced threats, including
zero-day
vulnerabilities and sophisticated malware.
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Phishing Detection: AI-powered email security solutions can identify and block
phishing
emails, protecting organisations from email-based attacks.
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Endpoint Security: AI-driven endpoint detection and response (EDR) solutions can
identify
and respond to threats on individual devices.
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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.
