AI and cybersecurity have become deeply interconnected as organizations face threats that evolve faster than traditional defenses can adapt. The scale of the problem is significant: the FBI's Internet Crime Complaint Center recorded total reported losses surpassing $16.6 billion in 2024, with email-based attacks among the costliest categories.

Machine learning and behavioral analysis now enable security teams to detect attacks that lack signatures or payloads, shifting protection from reactive pattern-matching to proactive anomaly detection. Learn how AI transforms threat detection, the types of AI used in security operations, the benefits and challenges of implementation, and what to consider when evaluating AI-powered solutions.

This article draws from insights shared in "Applying AI in Cybersecurity: The CISO Perspective," featuring Ariel Weintraub, CISO at Aon, and Mike Baker, CISO at DXC Technologies. Watch the full recording to hear how Fortune 500 security leaders are harnessing AI to protect their organizations.

AI in cybersecurity applies machine learning algorithms, behavioral analysis, and automation to detect, prevent, and respond to threats. Rather than relying solely on known threat signatures, AI-powered systems analyze patterns across users, devices, and communication channels to identify anomalies that may signal an attack.

This approach addresses a fundamental limitation of traditional security tools. Rule-based systems require manual updates and can only detect threats they've been programmed to recognize. AI systems learn continuously from data, improving accuracy over time and surfacing threats that lack known indicators of compromise.

Modern AI cybersecurity platforms analyze thousands of signals, including login patterns, email content, communication styles, and device behavior. By establishing baselines of normal activity for each user and vendor, these systems can flag deviations that warrant investigation without overwhelming security teams with false positives.

Understanding the differences between legacy and AI-driven approaches clarifies why organizations are shifting their security strategies.

Different AI technologies serve distinct functions within security operations. Understanding these categories helps organizations evaluate which capabilities matter most for their threat landscape.

Machine learning algorithms analyze historical data to identify patterns associated with malicious activity. Supervised learning models train on labeled datasets of known threats and benign activity, while unsupervised learning detects anomalies without predefined categories.

These models continuously improve as they process new data, reducing false positives and catching emerging attack variants.

Natural language processing (NLP) enables AI to understand the meaning and intent behind text-based communications. In email security, NLP detects social engineering by analyzing tone, urgency, and language patterns that differ from a sender's established communication style.

This capability is essential for identifying business email compromise attacks that contain no malicious links or attachments.

Behavioral AI builds profiles of normal activity for users, devices, and vendors by analyzing thousands of signals over time. Rather than looking for specific indicators of compromise, behavioral systems flag deviations from established patterns. This approach catches novel attacks that have never been seen before, including insider threats and account takeover attempts that use legitimate credentials.

The behavioral approach combines three layers of analysis:

• Identity awareness verifies that the sender is who they claim to be.

• Context awareness evaluates whether the request makes sense given the relationship and communication history.

• Risk awareness assesses the potential impact of the requested action.

Deep learning uses neural networks with multiple layers to process complex data and recognize sophisticated patterns. These models excel at tasks like image analysis for detecting fraudulent documents, voice pattern recognition for authentication, and identifying coordinated attack campaigns across multiple vectors.

Deep learning models require significant training data but achieve high accuracy on complex detection tasks.

Generative AI creates realistic simulations of attack scenarios, allowing security teams to test defenses against a wide range of potential threats.

These tools can predict likely attack paths based on historical patterns and help organizations understand vulnerabilities before attackers exploit them. However, generative AI also enables adversaries to create more convincing phishing messages and personalized social engineering attacks at scale.

AI delivers measurable improvements across detection accuracy, operational efficiency, and response speed. These benefits compound as AI systems learn from each environment they protect.

AI detects threats that traditional tools often miss by analyzing patterns across large volumes of data. Behavior-based detection surfaces anomalies that may signal emerging attacks, even when no known signature exists. Machine learning models refine themselves over time by learning from past decisions, improving accuracy and reducing false positives. This is particularly valuable for detecting socially-engineered attacks like business email compromise that contain no malicious payload.

AI helps cut through alert noise by filtering out low-priority events and highlighting what matters most. By grouping related events and suppressing false positives, AI reduces the number of alerts analysts need to review.

Smarter prioritization keeps teams focused, lowers burnout, and improves response times. Analysts get the context they need upfront, enabling faster and more confident decisions.

AI automates repetitive tasks across the security stack, from triaging alerts to correlating threat intelligence and executing incident response playbooks.

Automation ensures consistency while freeing analysts to focus on complex investigations and strategic planning. Common automation areas include initial alert investigation, threat intelligence analysis, security control validation, and risk-based vulnerability prioritization.

AI shifts security from reactive to proactive. By analyzing threat trends, attacker behaviors, and system weaknesses, AI can anticipate likely attack paths and recommend preventative actions. This helps teams focus defenses on the most relevant risks rather than relying solely on static best practices.

AI also powers automated threat hunting, scanning for indicators of compromise before damage occurs.

AI capabilities improve continuously as systems learn from new data. Self-learning algorithms incorporate analyst feedback without requiring manual rule updates. This makes it increasingly difficult for attackers to circumvent defenses because the system evolves alongside their tactics. The result is a security posture that adapts to the threat landscape and closes gaps before attackers can exploit them.

AI addresses specific security challenges across multiple domains. These applications demonstrate how machine learning and behavioral analysis translate into practical protection.

Email remains the primary attack vector for organizations, making AI-powered email security essential. AI analyzes sender behavior, communication patterns, and message content to identify phishing attempts, business email compromise, and credential theft.

Unlike signature-based filters, behavioral email security detects attacks that contain no malicious links or attachments by recognizing when a message deviates from established patterns.

Advanced systems build profiles for every user and vendor, flagging unusual requests like unexpected payment changes or urgent wire transfers from compromised accounts. This approach catches impersonation attacks, lookalike domain spoofing, and vendor fraud that bypass traditional secure email gateways.

AI detects compromised accounts by monitoring authentication events, login anomalies, and behavioral shifts. When an account exhibits activity inconsistent with its established baseline, such as logging in from a new location immediately after a normal session, AI systems flag the potential compromise and can trigger automated remediation like password resets or session termination.

This capability is critical because attackers increasingly use legitimate credentials obtained through phishing or credential stuffing. Traditional security tools may not detect anything malicious when an attacker uses valid credentials, but behavioral AI recognizes that the activity pattern differs from the legitimate user.

Enterprise security teams use AI to reduce alert overload, improve detection speed, and help analysts focus on high-priority incidents. Many organizations layer AI on top of existing SIEM tools to enrich alerts and filter noise before it reaches the SOC. AI also powers automated investigation workflows that gather evidence, analyze context, and prepare incidents for review before human analysts get involved.

Common tasks that AI handles include isolating compromised systems, collecting logs, and triggering standard response playbooks. Organizations using AI in their SOCs have reported significant reductions in false positive rates and recovered substantial time from manual triage, improving both operational efficiency and analyst retention.

AI learns network traffic patterns over time, allowing it to recommend appropriate policies and identify connections that warrant inspection. Network detection and response platforms use machine learning to identify lateral movement, command-and-control communication, and data exfiltration that might evade traditional perimeter defenses.

This approach reduces the time and manual effort required to create and maintain security policies across multiple networks. AI can help organizations implement and enforce segmentation strategies by automatically identifying workloads and recommending appropriate access controls.

With thousands of new vulnerabilities reported each year, organizations struggle to prioritize remediation efforts. AI-powered security solutions analyze vulnerability data alongside contextual factors like asset criticality, exploit availability, and network exposure to rank risks and recommend patching priorities.

This helps security teams focus limited resources on vulnerabilities that pose the greatest actual risk to their environment.

AI improves security outcomes, but effective implementation requires addressing several key challenges. Organizations need a thoughtful approach to realize the full value of AI in their security programs.

AI is only as effective as the data it learns from. Inconsistent or biased training data can lead to models that miss certain threats or generate false positives in specific environments.

Organizations need strong data governance, diverse and representative datasets, and continuous evaluation across different use cases. Regular audits help ensure models remain accurate as threat landscapes evolve.

AI systems can be vulnerable to adversarial inputs designed to trigger incorrect predictions. Attackers may attempt to poison training data, craft inputs that evade detection thresholds, or exploit model logic to force incorrect outputs.

Organizations can mitigate these risks by combining AI with traditional detection methods, using adversarial training to test model robustness, and monitoring systems for unexpected behavior in production.

When an AI system flags a threat, analysts need to understand why. Without explainability, teams may waste time chasing false leads or struggle to justify decisions during compliance reviews.

Effective AI security solutions provide visibility into the factors that contributed to a detection, helping analysts act quickly and confidently during investigations. Transparency also builds trust, encouraging adoption across the organization.

AI adoption introduces compliance considerations that most regulations weren't designed to address. Privacy regulations affect how behavioral data is collected and used. Industry-specific requirements in healthcare, finance, and government add additional constraints.

Organizations need to document how AI systems operate, make decisions, and handle sensitive data. Cross-functional coordination between legal, compliance, and security teams is essential for defining governance frameworks.

Attackers have access to the same AI technologies that defenders use. Large language models enable convincing phishing emails and personalized social engineering at scale.

Automated tools scan for vulnerabilities faster than human security teams can respond. This arms race means that AI-powered defense has become necessary to match the speed and sophistication of AI-powered attacks.

Successful AI adoption requires aligning technology capabilities with organizational needs and existing infrastructure. These considerations help security leaders evaluate and deploy AI-powered solutions effectively.

The right AI solution should integrate easily with existing infrastructure, adapt quickly to the environment, and deliver results that security teams can trust. Key capabilities to prioritize include:

• Integration readiness with existing SIEM, SOAR, and endpoint tools through APIs and native connectors

• Explainability that shows why threats were flagged and how decisions were made

• Continuous learning that adapts to the unique environment and incorporates analyst feedback

• Scalability under real-world data volumes, not just controlled test environments

• Deployment simplicity that minimizes friction and time to value

• Customizable controls that fit the organization's risk tolerance and workflows

Vetting an AI solution means going beyond demonstrations to understand how the system performs in real environments. Consider asking:

• What training data was used, and how similar is it to our environment?

• How does the platform defend against adversarial evasion techniques?

• What metrics demonstrate improvements over traditional tools in similar deployments?

• How does the model adapt when systems or workflows change?

• What visibility do analysts have into model decision-making?

• How quickly can the solution deploy without disrupting existing operations?

Effective AI solutions fit the organization's risk profile, resource constraints, and team maturity. Before selecting a platform, map critical assets, threat exposure, and compliance requirements. For lean teams, prioritize tools that reduce manual triage and improve analyst productivity. For compliance-heavy sectors, ensure the platform supports detailed audit trails and evidence preservation.

Consider the team's ability to manage the system. Highly flexible tools offer more control, but packaged solutions often deliver faster value. Organizations newer to AI may benefit from platforms that require minimal configuration while still providing strong detection capabilities.

AI continues to evolve, creating new opportunities and challenges for security teams. Several developments are shaping what organizations should prepare for.

AI is already automating portions of security workflows, but full autonomy is emerging. Systems that can detect threats, analyze context, and respond in real time with minimal human input could dramatically improve response times and reduce manual workloads.

This shift raises important questions about oversight and trust, especially when autonomous decisions affect critical infrastructure. Establishing clear policies and appropriate fail-safes will be essential.

As organizations adopt collaboration platforms like Slack, Microsoft Teams, and Zoom alongside email, attackers have more vectors to exploit.

Unified AI platforms that apply behavioral intelligence across all communication channels prevent attackers from simply shifting tactics when one path is blocked. Cross-channel visibility helps security teams understand threats in context rather than investigating each platform in isolation.

Quantum computing has the potential to break many current encryption standards. While commercial-scale quantum systems are still emerging, security teams should start identifying where quantum-vulnerable algorithms are used and evaluate emerging quantum-resistant encryption standards.

Governments and regulators are introducing new standards for responsible AI use. Legislation like the EU's AI Act shapes how AI tools can be deployed in security operations.

Organizations that embed compliance considerations early will be better positioned to meet evolving requirements. This means tracking legislation, updating risk frameworks, and ensuring AI decisions remain auditable and explainable.

The most effective security programs combine the speed of AI with the judgment of human analysts. AI handles scale by analyzing patterns, surfacing anomalies, and reducing noise. Security teams apply context and expertise to take decisive action. This partnership allows organizations to operate at machine speed while maintaining the nuanced decision-making that complex threats require.

AI systems that provide explainable detections, integrate with existing workflows, and adapt to analyst feedback strengthen this collaboration. When AI handles routine investigation and triage, analysts can focus on threat hunting, strategic planning, and responding to sophisticated attacks that benefit from human creativity.

Behavioral AI exemplifies this approach by analyzing the context behind every communication to detect threats that traditional tools miss while presenting findings in ways that help analysts act quickly. The goal is not to replace human judgment but to amplify it with intelligence that scales across the organization.

• AI in cybersecurity shifts defense from reactive signature-matching to proactive behavioral detection, enabling organizations to catch threats that lack known indicators of compromise before damage occurs.

• Behavioral AI provides a critical advantage by establishing baselines of normal activity for users, vendors, and devices, then flagging deviations that signal potential attacks like business email compromise, account takeover, and vendor fraud.

• Successful AI implementation requires solutions that integrate with existing infrastructure, provide explainable detections, and adapt continuously to each environment without requiring constant manual tuning.

• The most effective security programs combine AI speed and scale with human judgment, allowing analysts to focus on strategic threats while automation handles routine triage and investigation.

Abnormal's behavioral AI analyzes identity, context, and risk across every email and communication to detect threats that bypass traditional security tools. The platform deploys in minutes via API integration, requires no MX record changes, and enhances existing security infrastructure rather than replacing it.

Whether the goal is improving detection accuracy, reducing response times, or streamlining security operations, Abnormal helps organizations move from reactive defense to intelligent protection at scale.

Book a demo to see how behavioral AI transforms threat protection.