AI's proficiency at cybersecurity use cases has become a necessity for security teams striving to stay ahead of the scale and sophistication of today’s advanced threats. Yet it's estimated that more than 80% of AI projects fail.¹ There's no shortage of security vendors promising a wide array of AI capabilities to help teams achieve more … but which AI tools will actually help your SOC teams succeed?

Let's explore how you can make measurable improvements to your team's ability to detect and respond to cyber threats faster and with more precision and context — using the right AI tools.

Of all the security use cases for which AI has shown proficiency, its application for threat detection and incident response has proven to be especially strong.

In the following sections, see how your team can benefit from AI for threat detection and response, starting with AI-driven threat detection.

For security leaders, the mandate is clear: Detect threats faster, reduce false positives, and keep teams focused on what matters. Traditional detection approaches, rooted in static rules and signature-based alerts, can't keep up with today's dynamic landscape — especially considering adversaries themselves are using AI for their own efforts. In fact, phishing attacks have surged 1,200% since the rise of GenAI in late 2022.²

To defend against the surge in attacks, AI-driven threat detection brings powerful advantages to security operations. It identifies patterns across massive datasets, learns from behavioral baselines, and surfaces subtle or emerging cyber threats that traditional tools and processes often miss. More importantly, it reduces the operational drag of noise and alert fatigue — giving analysts time back and enabling the SOC to scale without burning out the team.

What are other ways AI can enhance detection?

At a scale that human efforts alone can’t realistically match, AI analyzes millions of events in real time to establish baselines for normal activity and surface anomalous activity that deviates from this norm. This can include:

• Detecting unusual login patterns (geo-velocity, time-of-day anomalies, suspicious user activity)
• Building context from correlation of multiple low-severity alerts across devices
• Spotting rare process executions or protocol usage
• Uncovering abnormal data access patterns (e.g., mass downloads)
• Identification of slow lateral movement or “low and slow” exfiltration

The AI advantage

AI connects the dots across noisy logs to surface early signs of complex attacks and catches novel or stealthy threats that traditional rules or signatures miss.

It’s not enough to have strong detection capabilities. Teams also need to be able to respond quickly. Traditional incident response processes are manual, fragmented, and dependent on limited human capacity. The right AI tools help teams break that cycle.

By automating repetitive tasks, enriching alerts with actionable context, and guiding analysts through triage and remediation, AI transforms incident response into a faster, more scalable, and more consistent function. It empowers teams to move from reaction to resolution with clarity and confidence — without adding headcount or burning out staff.

From triage assistance to step-by-step workflow guidance, here’s how AI integrates into the key stages of incident response.

Accelerating triage

Fast response can make the difference between threat containment and escalation. AI-driven response processes identify patterns across alerts and connect them into larger incident narratives. For example, AI-driven processes can:

• Group alerts that are part of the same attack chain (phishing → credential use → lateral movement)
• Suppress duplicates or false positives using learned behavior patterns

The AI advantage

AI-driven processes reduce alert fatigue and enable analysts to focus on priority incidents rather than noisy one-offs.

Context enrichment

By instantly enriching alerts with contextual data, AI-driven response accelerates triage — connecting users, assets, and potential attack paths — without all the manual sleuthing. Analysts can now move confidently through response workflows in a fraction of the time. AI helps by:

• Automatically enriching alerts with context (e.g., threat intel, geolocation, asset criticality)
• Summarizing event timelines and root cause analysis in natural language
• Highlighting affected users, systems, and paths of lateral movement

The AI advantage

AI elevates the skillset and understanding of every analyst while reducing time spent gathering basic context.

Automating playbooks

Unassisted by AI, incident response can be slow, error-prone, and inconsistent. AI-automated playbooks trigger the right actions based on alert context, threat severity, and historical outcomes. This ensures faster containment, reduces analyst workload, and drives more consistent response across the SOC.

For example, AI-driven response:

• Kicks off automated response actions based on detection type (via SOAR or built-in automation actions)
• Uses conditional logic to escalate or contain based on severity and context
• Learns from past incidents to optimize playbook decisions

The AI advantage

AI allows for automatic and accurate quarantining of infected endpoints, disabling of compromised user accounts, and blocking of malicious IPs or domains.

Copilot guidance

Even experienced analysts can struggle with uncertainty during fast-moving incidents. AI-driven guidance acts as a real-time copilot, suggesting investigation steps, surfacing similar historical cases and data, and highlighting the next best steps to take at each phase of a response. It accomplishes this by:

• Recommending context-aware actions based on the specific alert type, affected systems, and past resolution paths
• Linking related incidents and threat intelligence to help analysts quickly understand scope, tactics, and potential impact
• Providing natural language summaries for each suggestion, making decisions easier to validate and execute

The AI advantage

With AI-driven guidance, analysts can make accelerated, consistent, and accurate decisions during incident response, elevating analyst productivity and confidence.

Of course, even with the best AI tools on the market, a team’s ability to detect and respond is only as good as the data they can analyze. Fortunately, AI again shows strong ability to help teams ingest, normalize, and analyze their data like never before.

In the next section, see how AI is helping teams onboard data and ease SIEM migration to maximize detection and response through improved visibility.

¹ Rand, "The root causes of failure for artificial intelligence projects and how they can succeed," 2024.

² McKinsey & Company, "AI is the greatest threat—and defense—in cybersecurity today. Here's why.," 2025.