“AI powered threats aren’t one from the future, they are right here already! SOC teams in response need to unleash the power of AI counter these AI threats, autonomous SOC shouldn’t be limited to playbook blocs, its more about autonomy, AI powered automated response with human on the loop” - B Mani Shankar, Global Manager, MDR and Assurance services, Cybersecurity, HCLTech
Every day, security teams face a tidal wave of telemetry, alerts and dashboards. Traditional SIEMs (Security Information and Event Management systems) were built to centralize logs and correlate events, but today’s data volume and attack speed often overwhelm them. This slows down detection and delays response when minutes matter.
The introduction of AI and its subsets, including Generative AI (GenAI) and Agentic AI, to the world of cybersecurity are changing this by spotting unfamiliar patterns, surfacing true risk and enabling autonomous action.
Why it matters:
- Alert fatigue: Teams chase false positives instead of real threats
- Scale and speed: Threats move faster than manual investigation
- Novel attacks: Signature-based rules miss unknown tactics
Across the market, vendors have embedded AI for years, which was often 'traditional' machine learning. Now they’re layering in GenAI for analysis and guidance. Yet, fully autonomous SOCs remain aspirational, for now. Most organizations still operate human-in-the-loop and are cautiously testing human-on-the-loop models where automation proceeds and humans can tune or roll back in session. A business-ready approach blends automation with explainability, governance and trust (AI TRiSM).
Why is AI important in modern threat detection?
"AI augments threat detection by identifying novel threats by processing vast amount of data and negating them through automated action, reducing human error and response time. AI thrives where humans struggle. The technology can sift through petabytes of signals to find weak indicators, correlating behaviour across endpoints, identities, cloud, and SaaS, and trigger containment in seconds, not hours" - B Mani Shankar, Global Manager, MDR and Assurance services, Cybersecurity, HCLTech
Leading sources emphasize that AI speeds detection across expanding attack surfaces and reduces manual toil.
The limitations of traditional SIEMs
- Overload of data and alerts: SIEMs collect everything and tuning takes time. The result stretches Mean Time to Detect (MTTD).
- Blind spots for novel or sophisticated threats: Static rules and signatures struggle with new tactics.
- Delayed response due to manual processes: Human-only triage and playbook execution can’t keep pace with automated campaigns
How AI overcomes SIEM challenges
- AI-powered analytics scale to your data: Models ingest high-volume telemetry and flag meaningful outliers in near-real time
- Machine learning finds patterns people miss: Behavioral baselines reveal subtle anomalies across users, devices and workloads
- Automated playbooks accelerate containment: From isolating endpoints to resetting credentials, actions execute in seconds with audit trails
Traditional SIEM versus AI-enhanced SIEM
|
Traditional SIEM |
AI-Enhanced SIEM |
| Rules and signatures, heavy tuning | Learns baselines, flags anomalies automatically |
| Manual triage across tools | Correlated insights across endpoints, cloud and identity |
| Human-executed response | Automated playbooks with human oversight |
How does AI threat detection work?
"AI threat detection centres around anomaly detection, behavioural analytics and pattern recognition " - B Mani Shankar, Solutions Architect, MDR and Assurance services, Cybersecurity, HCLTech
AI continuously learns what's “normal” across an organization's environment, then surfaces deviations that matter, such as failed logins at odd hours, unusual data access, lateral movements or rare process launches, and then ties them together into a narrative and initiates the next best action.
Key components of AI threat detection
- Anomaly detection: Spots deviations from typical patterns
- Behavioral analytics: Profiles users, entities and services to catch risky behavior, like an HR account accessing source code
- Automated playbooks: Pre-approved steps executed immediately, with notifications to owners
AI-powered tools monitor multi-channel signals, like network, identity and SaaS, and correlate them to detect phishing, ransomware precursors or insider misuse, before quarantining or revoking access as needed.
The role of GenAI and Agentic AI in cybersecurity
- GenAI: Generates summaries, hypotheses and recommendations, explains why something looks risky, drafts response notes and stakeholder comms
- Agentic AI: Executes steps autonomously within defined guardrails and allows humans to pause/roll back mid-action
GenAI vs. Agentic AI
|
GenAI |
Agentic AI |
| Produces insights and recommendations | Executes actions within policy guardrails |
| Speeds analysis and documentation | Shrinks dwell time with real-time containment |
| Human-in-the-loop by default | Human-on-the-loop with explainability and rollback |
Analyst firms and industry groups highlight the need for governance and controls, especially as GenAI connects to internal data. Risks like prompt injection and data leakage must be managed under an AI TRiSM framework.
What are the benefits of AI-driven threat detection?
- Faster identification of threats: Continuous learning shortens MTTD by surfacing unusual behavior quickly, even without prior signatures
- Reduced false positives and alert fatigue: Behavior-aware analytics prioritize likely attacks so analysts spend time where it counts
- Real-time automated responses: Pre-approved actions cut Mean Time to Respond (MTTR), limiting blast radius and business impact
- Improved accuracy in detecting novel threats: Models detect patterns that don’t match known rules, catching emerging tactics sooner
- Lower incident response workloads for cybersecurity teams: Automation handles repetitive steps, letting teams focus on complex investigations and stakeholder decisions
Challenges and limitations of AI in threat detection
"While AI improves threat detection, challenges include reliance on data quality, potential biases in algorithms and high implementation costs" - B Mani Shankar, Global Manager, MDR and Assurance services, Cybersecurity, HCLTech
AI’s effectiveness within cybersecurity depends on trustworthy data, clear guardrails and the right operating model. Analysts warn that linking large language models to internal repositories without robust controls can expose sensitive data; governance and monitoring are essential. Most organizations aren’t ready for fully autonomous response and lean on human-in or on-the-loop approaches.
Common challenges
- Data quality and visibility: Gaps in asset discovery, shadow AI or incomplete telemetry limit model accuracy
- Algorithmic bias and drift: Models can over- or under-prioritize certain behaviors if inputs aren’t representative
- Integration complexity: Stitching AI into existing SIEM/SOAR, identity and cloud tools takes planning and change management
How to address these challenges
|
Challenge |
Solution |
| Incomplete or poor-quality data | Expand discovery/telemetry, validate baselines and monitor model performance and drift |
| Bias and explainability gaps | Apply AI TRiSM controls, require rationale for detections and perform periodic fairness checks |
| Integration with existing systems | Start with high-value playbooks, run in “recommend-only” mode and graduate to human-on-the-loop with rollback |
Industry guidance stresses AI governance and staged automation to balance speed with safety.
AI for network security and monitoring:
Use cases
"AI as on today is used in network security for intrusion detection, data exfil detection, service attacks detection, etc." - B Mani Shankar, Global Manager, MDR and Assurance services, Cybersecurity, HCLTech
Intrusion detection and prevention
- Real-time monitoring of network traffic to surface lateral movement or command-and-control beacons
- Blocking unauthorized access attempts via adaptive policies informed by behavioral analytics
Phishing and ransomware mitigation
- Phishing: Natural language processing (NLP) analyzes emails or messages to flag brand impersonation, tone anomalies and QR-phish, across email and collaboration apps
- Ransomware: Early indicators trigger isolation and credential resets to contain spread
Insider threat detection
- Behavior analytics spot unusual data access, privilege escalation or exfiltration by trusted accounts; malicious or negligent. Actions can auto-pause risky sessions while notifying business owners
Operating model: Human-in-the-loop to human-on-the-loop
Moving forward, a pragmatic path is to propose changes automatically, require human approval at first, then progress to execute with oversight where engineers can pause or roll back. This approach preserves control while capturing automation’s speed.
Gartner emphasizes that a fully autonomous, “lights-out” SOC isn’t a near-term destination for most. Instead, organizations should build maturity in discovery, explainability and governance before widening automation’s scope.
Key takeaways
- SIEM limits are real: Traditional SIEMs struggle with data complexity and delayed responses.
- AI adds speed and precision: AI-driven threat detection uses machine learning, anomaly detection and automated playbooks to address these challenges.
- Automation reduces dwell time: Benefits include faster threat identification, reduced false positives and real-time containment.
- Governance matters: Challenges like data quality and algorithmic biases can be mitigated with proper governance practices.
FAQs
1) What is AI-powered threat detection?
AI-powered threat detection uses machine learning and advanced analytics to identify cybersecurity threats in real time.
2) How does AI improve SIEM systems?
By handling large data volumes, reducing false positives with behavioral context and automating responses to cut MTTR.
3) What is anomaly detection in cybersecurity?
It flags deviations from normal behavior in network or user activity that may signal threats.
4) What are automated playbooks in threat detection?
Predefined response actions, such as isolate endpoint and revoke token, are triggered by detections for faster containment.
5) What’s the difference between GenAI and Agentic AI in cybersecurity?
GenAI creates insights and recommendations, while Agentic AI executes containment actions within guardrails, with human oversight.
6) What are the main challenges of AI in threat detection?
Data quality, algorithmic biases and integrating AI with existing systems and processes.
7) How can AI help with phishing and ransomware attacks?
NLP detects phishing cues across email and collaboration tools, while behavior analytics spots ransomware precursors and isolates affected systems.
