In recent times, the average dwell time for cyber threats has exceeded 200 days in organisations without real-time monitoring. While security teams face thousands of daily alerts, including many false positives, genuine threats slip through unnoticed.
Traditional monitoring solutions fall short, and in response, enterprises have experimented with AI in cybersecurity. Research suggests that the global market for AI-driven cybersecurity will grow to $135bn by 2030. This transformation has the capability to take cybersecurity from reactive processes to proactive threat detection.
challenges in modern cybersecurity
The complexity of the cyber ecosystem poses significant hurdles for enterprises. Traditional security measures, reliant on manual processes and static rules, struggle against threats like ransomware, phishing, and advanced persistent threats. Key challenges include:
increasing attack sophistication
Static defences and periodic scans cannot keep up with modern attack speed and sophistication, leaving organisations vulnerable to zero-day exploits and polymorphic malware that evade traditional signature-based detection.
alert fatigue
Security teams receive thousands of alerts daily, overwhelming human analysts and causing critical threats to be missed among false positives and low-priority notifications.
delayed responses
Manual analysis creates dangerous delays, allowing attackers to move laterally through networks undetected for extended periods whilst exfiltrating sensitive data.
limited visibility
Legacy systems struggle to provide unified visibility across hybrid cloud environments, remote endpoints, IoT devices, and third-party integrations that expand attack surfaces.
Real-time monitoring is essential because cyberattacks can compromise systems within minutes, making traditional reactive approaches insufficient for modern threat landscapes.
how AI transforms real-time threat monitoring
Artificial intelligence revolutionises cybersecurity through machine learning, behavioural analytics, and automation to enable unprecedented speed and accuracy in threat detection. AI-enhanced systems deliver critical capabilities like:
behavioural analysis and anomaly detection
AI establishes baseline behaviours for users, devices, and applications, immediately flagging suspicious deviations that may indicate compromise. Unlike signature-based systems, AI learns what normal activity looks like and identifies anomalies that may indicate zero-day attacks.
intelligent correlation and prioritisation
AI connects seemingly unrelated events across multiple systems to identify sophisticated, multi-stage attacks that would remain invisible to traditional tools. Advanced algorithms reduce false positives while ensuring genuine threats receive immediate attention.
automated incident response
AI systems work 24/7 without fatigue, providing consistent vigilance, especially when many attacks are launched outside business hours. Automated playbooks isolate compromised systems, revoke credentials, and block malicious activities within seconds.
predictive threat intelligence
AI analyses historical attack patterns and current threat intelligence to anticipate potential vulnerabilities, enabling proactive defence measures rather than purely reactive responses to active threats.
key benefits of using AI in cybersecurity
Adopting AI for real-time threat monitoring delivers measurable advantages aligned with enterprise trust and safety requirements:
rapid threat mitigation
AI-driven real-time monitoring significantly reduces risk by catching threats earlier and enabling faster response, minimising potential financial and operational damage.
enhanced detection accuracy
Machine learning algorithms dramatically reduce false positives and improve the detection of sophisticated threats that bypass traditional signature-based defences.
operational efficiency
Real-time analysis enables quicker detection and response to cyber threats, freeing security teams to focus on strategic initiatives rather than manual alert triage.
scalable protection
AI adapts to growing data volumes and emerging attack vectors, ensuring long-term resilience across expanding digital infrastructures and multi-cloud environments.
regulatory compliance
Automated monitoring and detailed audit trails support adherence to frameworks such as GDPR, HIPAA, and industry-specific regulations, effectively reducing compliance overhead.
implementation best practices for enterprise success
Successful use of AI in cybersecurity implementation requires a structured approach combining advanced technology with organisational readiness:
comprehensive assessment and planning
Conduct thorough security audits to identify vulnerabilities and prioritise AI integration areas. Modern threat detection requires tools that provide continuous real-time monitoring and surface potential security issues.
integration with existing security infrastructure
Ensure seamless connectivity with SIEM, endpoint detection, and network monitoring solutions to maintain unified threat visibility whilst preserving existing security investments and avoiding technology silos.
team enablement and training
Equip security personnel to interpret AI-generated insights and respond effectively to automated alerts, ensuring optimal human-AI collaboration and maximising return on technology investments.
continuous optimisation and refinement
AI platforms continuously evolve by learning from past incidents and environmental changes, getting smarter over time. Regular model refinement and tuning ensure sustained effectiveness against evolving threats.
how can Infosys BPM enhance enterprise cybersecurity?
The comprehensive trust and safety suite of services from Infosys BPM integrates AI-powered threat detection with machine learning and behavioural analytics to deliver rapid and automated responses. Through our managed cybersecurity services, enterprises can access expert analysts, advanced AI technologies, and proven incident response methodologies. This enables organisations to strengthen their security posture and focus internal resources on core business objectives.
