AI-powered cloud SIEM: Real-time threat intel boosts defences

Cyber attacks in 2023 continue to gain traction. As dwell times lessen, cyber crime rates are developing quicker, leading to higher frequencies of cyber attack attempts. But companies at large and small scales are fighting back, opting to adopt higher protective measures with expanded cyber security capabilities. 

For example, Google has recently switched to AI-backed cyber controls that utilise zero-trust security access restrictions, digital sovereignty, and threat defence to protect Google’s digital workspaces. AI has become a key component in addressing operational technology (OT) system threats as well. 

With advanced capacities for analysing large quantities of data in one go, and enhanced predictive capabilities for identifying potential threats and weak points across a broad security system, AI and machine learning are valuable tools to protect cyber infrastructures going forward.

In this article, we will take a look at the revolutionary potential of AI-powered cloud security information and event management (SIEM) solutions, which work continuously to protect vital digital systems. 

How cloud SIEM works

SIEM refers to a security solution intended to identify and prevent cyber threats from becoming actualised as full-blown cyber attacks. SIEM allows organisations to analyse possible security vulnerabilities and weak points, providing an opportunity for organisations to address these security glitches before they result in successful, disruptive cyber attacks. 

SIEM solutions work by monitoring user access to identify unusual user behaviours that could indicate a possible threat to cyber security. SIEM used to use log management tools to enact system monitoring and analysis in real-time. The term “SIEM” itself was coined by Gartner in 2005 to refer to a combination of security information management (SIM) and security event management (SEM) practices. 

Nowadays, SIEM has become increasingly sophisticated, incorporating the latest in cutting-edge technologies to provide advanced level security coverage and real-time threat analysis. Rather than a software located in an external device, cloud SIEM is a security platform based in the cloud that provides comprehensive security coverage for an organisation’s systems. 

Cloud-based SIEM security solutions prevent false positive identification of security risks, providing enhanced threat identification capabilities. They use log analytics monitoring that can be scaled up or down according to an organisation’s needs. Cloud SIEM platforms offer a simplified version of security operations centre (SOC) actions, providing cross-platform integrated monitoring capabilities and automation of advanced security monitoring and machine learning algorithm-based analysis. 

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How machine learning algorithms respond to cyber threats in real-time

Cloud-based SIEM has become an essential component of most contemporary security systems, often in conjunction with other cyber security platforms. Cloud SIEM relies on the latest revolutionary technologies, incorporating AI and machine learning to provide enhanced security coverage and up to the minute cyber security threat detection and responses. 

Continuous monitoring capabilities

In cloud SIEM protocols, machine learning algorithms work around the clock, continuously monitoring network data and user behaviour to identify potential cyber threats. Where human security teams might accidentally overlook specific indicators of compromise, AI algorithms enact continuous monitoring, making it highly unlikely that a suspicious incident or user behaviour will slip through the cracks, so to speak. 

Vast data processing capacity

AI algorithms can be programmed to assess huge amounts of data almost instantaneously, which provides a monumental advantage when it comes to staying on top of potential cyber threats in real time. 

AI machine learning algorithms in a SIEM platform can also analyse cloud log data in real-time to be able to assess whether any anomalies could indicate a possible threat, violation of organisational security policies, or other security incident. 

Phishing prevention

In cloud SIEM platforms, AI models can specifically seek out phishing attempts, analysing written communication content, including emails and messages to identify compromised links and attachments. AI models can analyse user behaviour patterns to assess where there may be a phishing attempt, alerting the relevant security team members to intervene where necessary. 

This is a key facet of cyber security prevention across all organisations today. According to some sources, social engineering attacks are responsible for an astonishing 98% of cyber attack attempts today. So AI pattern identification abilities are an invaluable resource in preventing phishing and social engineering attack attempts that could result in costly and damaging security or data breaches. 

Updating security compliance protocols

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With its advanced capacities to recognise and identify patterns according to programmed norms, rules and codes of behaviour, AI is also able to ensure that all of a particular organisation’s security protocols and procedures are compliant with up-to-date security rules and regulations. 

AI tools can identify issues with compliance and produce reports revealing any non-compliant organisation-wide activities, thus ensuring that the security protocols and activities will remain in line with current security standards. 

An organisation’s security can then be strengthened with hopefully simple and yet highly effective measures, such as using secure PDF tools that come with the ability to encrypt documents via secure encrypted digital signatures, which are harder to penetrate and replicate. This switch can help mitigate the effects of any potential cyber breaches and keep an organisation’s internal data secure. 

Building on historical cyber attack patterns

AI uses historical security breach patterns to build a foundational knowledge base of suspicious behavioural patterns and unusual user activity, allowing the AI models to build more sophisticated security responses, breach mitigation procedure recommendations, and incident prevention as time goes on and more data is accumulated. 

AI algorithms in SIEM platforms access security incident reports from various sources, combining this data to provide a more comprehensive overview of possible interdependent security events from separate incidents or organisations. 

AI-driven threat intel enhances security team efficiency

Threat intelligence software powered by advanced AI and machine learning empowers human security teams to make proactive adjustments to the organisation’s security protocols. 

Enhanced security recommendations

With the ability to process huge amounts of data quickly, and to identify and recognize complex patterns from across interconnected platforms and incidents, AI threat intelligence can provide advanced recommendations and alerts to security teams, providing security teams with the efficient baseline alert to take necessary preventive actions. 

Tracking user and entity behaviour analytics

While other security systems may be fooled into accepting unauthorised network login attempts that convincingly mimic authorised user logins, AI models in SIEM systems utilises user and entity behaviour analytics (UEBA) to track and identify anomalous behaviour patterns or unfamiliar actions by normal authorised users. 

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Providing a more comprehensive approach to analysing user behaviour, UEBA prevents wily attackers from circumventing normal security protocols by taking a more holistic approach to assess user behaviour. 

Actionable insights

With advanced AI and machine learning tools, security teams are armed with valuable actionable insights, allowing teams to stay on top of any potential cyber threats and continue to protect private data and cloud-based assets. 

Collaboration across security teams

AI and machine learning algorithms in SIEM platforms can be used to promote collaboration across security teams, as security teams in different organisations contribute to a shared knowledge database of threat insights, security events, up-to-date indicators of compromise (IOCs), and ongoing cyber crime investigations that can benefit all parties involved. 

These interconnected datasets are presented in the SIEM platform in easily navigable visualisations that provide layers of analysis that can help security teams quickly read the necessary details of a specific cyber attack. These advanced visualisation tools help simplify complex interconnected webs of cyber attack, combining various layers of attack vectors, data, and complicated patterns of behaviour to create a more efficient map for security teams to utilise. 

Final thoughts

With its cutting-edge abilities to continuously monitor data and user behaviour, instantaneously analyse and assess patterns across expansive data sets, and provide advanced level security protocol recommendations and actionable insights, AI and machine learning capabilities provide an essential resource to today’s security teams. 

Gathering valuable data and building on patterns of previous cyber attacks through interconnected SIEM cloud platforms is a key part of creating a broad, comprehensive security map across all organisations, allowing security teams to build on their knowledge base and incident response procedures by collaborating across agencies and contributing AI-gleaned knowledge to a sophisticated and shared database. 

Unprecedented speed and monumental computing capabilities allow today’s security teams to stay ahead of the latest cyber security attacks in development, efficiently identifying and rectifying any gaps or vulnerabilities across the entire security landscape while also providing real-time updates on user behaviour, potentially suspicious activity, and unauthorised login attempts across all organisational systems and digital landscapes. 

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