AI for cyber threat detection and predictive insights

Threat detection today and the need for automation

Technology has transformed business, but new systems, cloud environments, and endpoints have increased complexity and vulnerability. This growth has driven a surge in cyber threats. In 2025, over 560,000 new cyber threats are discovered daily, a 60% increase from 2020. The global cost of cybercrime is projected to reach $10.5 trillion, nearly triple the losses five years ago.

Organizations are struggling to keep up. Data volumes are growing, remote work complicates monitoring, and attackers leverage AI and automation to move faster than traditional defences. Security teams must monitor thousands of layers, including devices, applications, code repositories, and user accounts, while relying heavily on manual processes.

According to Panaseer’s 2025 Security Leaders Peer Report, 72% of security leaders believe they could prevent more breaches if they spent less time on manual reporting. Visibility gaps persist, with 70% of decision-makers reporting that their teams lack the analytical tools needed to fully understand or report on risk. Unknown unknowns remain across asset inventories and controls.

The problem is clear. Attackers are outpacing human response, and traditional methods are insufficient. Organizations need faster, more accurate, and proactive ways to detect threats, reduce blind spots, and manage the growing volume and sophistication of cyber risks.

How Panaseer uses AI to transform threat detection

AI and machine learning are transforming threat detection by rapidly identifying patterns and surfacing the root causes behind changes. By analysing network traffic, user behaviour, and system logs, AI can classify activities as normal or abnormal and detect anomalies.

Sophisticated anomaly detection algorithms establish baselines for normal behaviour over time and flag deviations as potential threats. Big data analytics accelerates detection further by processing massive datasets from network logs, user activity, and threat intelligence feeds, training AI models to spot risks faster and more accurately.

Panaseer AI takes this a step further by combining AI with deep, structured visibility across the organization. This turns reactive monitoring into predictive defence with:

• Complete Asset Inventory: Automatically discovers every device, application, and user—including unknown assets—giving AI a comprehensive view.
• Context and Lineage: Understands the flow of information across systems, enabling analysts to trace anomalies to their root cause.
• Adaptive Thresholds: Learns what constitutes meaningful deviation, cutting through noise and reducing false positives.
• Continuous Learning: AI detects patterns across millions of control records, behaviors, and events, improving accuracy over time.

All these capabilities come together in Key Drivers, a new AI feature the in Panaseer platform. This delivers insights in clear language, enables drill-down by device, business unit, or user group, and directs on investigation paths.

By embedding AI across Continuous Controls Monitoring (CCM), Panaseer empowers security teams to detect threats faster and understand risks more deeply, moving organizations beyond visibility to intelligent, automated and predictive security.

Use cases for AI threat detection

AI-powered threat detection isn’t just about finding more alerts, it’s about giving analysts speed, context, and prioritization to stop what matters most. Here’s where Panaseer’s Key Drivers could have changed the outcome of major breaches:

• Faster Detection 
  AI correlates and analyzes data in real time for quicker anomaly detection. 
  💡 MOVEit Transfer (2023): Unusual access patterns went unnoticed for days. With Key Drivers, analysts could have flagged anomalies immediately and contained compromised accounts before exfiltration.
• Improved Threat Intelligence 
  AI integrates contextual business data for deeper insights. 
  💡 23andMe (2023): Credential-stuffing attacks exposed millions of records. Panaseer’s data would have highlighted unusual access behavior early, surfacing exposure risks before mass exploitation.
• High-Volume Threat Processing 
  AI scales monitoring across networks, endpoints, and cloud infrastructure. 
  💡 JD Sports (2023): Customer and financial data were compromised. Panaseer’s AI would have correlated alerts across multiple sources, detecting the breach before widespread impact.
• Risk Prioritization 
  AI cuts noise by surfacing high-impact threats first. 
  💡 MGM Resorts (2023): Attackers gained access through social engineering and lateral movement. Key Drivers would have traced unusual account activity to its source, ensuring immediate prioritization.

The future of AI-driven threat detection with Panaseer

AI in cybersecurity is moving from experimentation to expectation. As regulations like the EU AI Act and ISO/IEC standards evolve, transparency, accountability, and explainability are becoming non-negotiable for enterprise security tools. At the same time, Continuous Controls Monitoring is being even more recognized as a critical layer of assurance. Panaseer is driving this charge in AI-driven cyber risk management, ensuring that insights remain grounded in verifiable data and measurable controls.

By embedding AI directly into CCM, Panaseer not only automates detection but also makes insights auditable, contextual, and aligned with business risk. Key Drivers exemplifies this commitment, turning billions of raw data points into clear investigation paths, showing precisely why anomalies occur, and ensuring analysts can make the right decisions.

This is where the future lies: secure, transparent automation that balances speed with oversight. For enterprises facing unprecedented volumes and complexity of cyber threats, Panaseer delivers more than efficiency, it delivers clarity, confidence, and control. 

Want to know more or test the product? Take a tour of Key Drivers or get a demo with a member of the team today.