AI Threat Detection

Artificial intelligence (AI) is everywhere, so it’s logical that machine learning models are being deployed to recognize patterns that indicate cyber threats — alerting organizations to anomalies that could indicate attacks in real time. Of course, AI can’t solve all cybersecurity problems, especially when adversaries employ it just as defenders do. And secondary challenges task teams with honing their models and integrating with an existing suite of tools. We’re looking at AI threat detection: what it is, how it works, how to solve secondary challenges, and how it fits into cybersecurity strategies today.

What is AI Threat Detection?

AI threat detection uses machine learning to identify, analyze, and respond to cyber threats, improving security by detecting novel behavioral patterns and known attack signatures alike. AI threat detection includes:

• Anomaly detection: AI models establish a baseline for normal behavior, from user logins to network traffic, and use unsupervised learning like clustering and isolation forests or statistical methods to detect deviations that could mean threats.
• Behavioral analysis: AI uses both supervised and unsupervised learning to track how users, devices, and processes behave over time, so if an account beings accessing sensitive data at odd hours, for instance, reinforcement learning or Bayesian models can flag it.
• Threat intelligence integration: Machine learning models ingest and correlate teal-time threat feeds, as from MITRE ATT&CK, to identify known indicators of compromise (IoCs) and predict new variants by analyzing historical patterns.
• Automated response: AI-driven automation, as in Security Orchestration, Automation, and Response (SOAR) platforms, uses predefined playbooks and reinforcement learning to decide whether to quarantine a device, block an IP, or escalate alerts.
• Continuous learning: AI models retrain on newly discovered threats, refining their accuracy over time.
• Multilayered monitoring: AI combines data from multiple layers, including network, cloud, and endpoint, and using ensemble learning techniques, cross-references security events across attack surfaces to detect multi-stage threats better.
• Explainability and transparency: Models should be transparent, and AI threat detection increasingly uses Shapely Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to demonstrate why events were flagged.
• Integration with existing tools: AI threat detection can pair with SOAR, Security Information and Event Management (SIEM), Extended Detection and Response (XDR), and Cloud-Native Application Protection Platform (CNAPP) using API-based machine learning pipelines. AI can analyze logs, telemetry, and alerts in real time to correlate behaviors and piece together complicated attacks.

Benefits of AI Threat Detection

Its components work together to provide better security than before the days of artificial intelligence. Learning models, after all, ingest large amounts of data and correlate events more easily and quickly than manual analysis. That gives organizations an edge, solving the problem of correlating their vast amounts of data in meaningful ways, on a reasonable schedule. But it also gives attackers new avenues to breach networks.

AI is a top cyber threat to look out for in 2025, according to the World Economic Forum, with 66% of organizations seeing AI as the biggest game changer this year, but with just 37% able to assess new AI tools before use.

Beaches continue at historic levels, leading teams to explore the benefits of AI in fighting off new attacks. The primary benefits they can expect include:

Speed and Efficiency in Threat Detection

AI accelerates cyber threat detection since it comes with the power to analyze huge amounts of security data in real time. Unlike traditional rule-based systems that use predefined signatures, AI continuously analyzes behavior patterns and anomalies to identify malicious activities, even previously unknown ones, in fractions of a second. Automated response mechanisms like those in integrated SOAR platforms can then take immediate action. AI ultimately reduces dwell time, reducing the likelihood that attackers gain a foothold in an organization’s network.

Accuracy and Adaptability

One of AI’s primary strengths is its ability to minimize false positives while continually adapting to new cybersecurity threats. Machine learning models refine their accuracy by learning from historical security events and evolving attack techniques. So, while traditional tools generate volumes of alerts, many of them benign, AI-powered solutions can differentiate more conclusively between legitimate anomalies and real threats. Further, zero-day threats and advanced persistent threats (APTs) find they’re unable to bypass security controls of AI-powered detection as they were with traditional defenses.

Scalability and Reduced Team Burden

Cloud-based computing has meant an ever-moving attack surface that can scale exponentially. That’s great, but not when cybersecurity can’t do the same. AI-powered security tools help. They can analyze billions of logs and network activities without bottlenecks, so scaling becomes a non-issue. They also reduce team workload by automating repetitive tasks like log analysis and event correlation. Ideally, teams can focus on high-priority and complex investigations.

AI Threat Detection to Thwart AI-Based Attacks 

AI helps, but it doesn’t create an inherent advantage unless security teams continuously adapt to new attacks. After all, if attackers and defenders both use AI, AI security stands to merely escalate costs. But where AI does give organizations an advantage is in its ability to detect new attack patterns and potential threats, not just disrupt old ones at scale. 

Here are common AI-driven attacks and how AI empowers teams to fight back:

Attack Type	Attacker Approach Using AI	AI Security Counter Measure	Defense Tool Example
Phishing and Deepfakes	AI-generated emails, voice cloning, and deepfake videos make phishing harder to spot.	AI analyzes writing style, voice/audio anomalies, and video for manipulations.	Email Security AI
Self-Mutating Malware	AI-driven malware changes signatures and behaviors to evade detection.	AI focuses on behavior analysis instead of signatures to detect unknown threats.	EDR/XDR AI or CNAPP with behavior analysis
Automated Vulnerability Scanning	AI scans for unpatched software, misconfigurations, and weak cloud settings faster than humans.	AI security platforms prioritize patches based on real-world exploitability.	CNAPP
AI-Assisted Password Cracking	AI predicts passwords by analyzing human behavior and common patterns.	AI-powered access controls enforce MFA, detect unusual logins, and flag brute-force attempts.	Identity Security Tools: Multi-Factor Authentication (MFA), Privileged Access Management (PAM), and CNAPP
AI-Guided Exploitation and Reconnaissance	AI automates finding weak spots in cloud environments and applications.	AI-powered security scanning detects risks before attackers do and locks down misconfigurations.	Cloud Security AI, i.e., CNAPP

Combining tools can be a key way to protect multiple layers of an organization’s infrastructure. 

For example, in phishing and social engineering attacks, the primary threats are AI-generated emails, voice cloning, and deepfake scams. Combining email security AI and identity security (MFA and PAM) can help catch attempts before they reach recipients. If phishing emails reach inboxes, MFA and adaptive authentication prevent unauthorized access by requiring additional verification. And PAM solutions restrict privilege escalation to limit the damage compromised identities can have.

The combination works best for thwarting business email compromise (BEC), executive impersonations, and credential theft.

Other combinations work best for other types of attacks. To neutralize AI-mutating malware and automated exploits in cloud workloads EDR/XDR with CNAPP is a combination that can hone in on endpoint and runtime behavior, detecting AI-generated malware even with no known signature.

In AI-powered credential attacks, like brute force attacks, credential stuffing, and reconnaissance, combine identity security with CNAPP. AI-enhanced MFA solutions help detect and block automated login attempts while PAM prevents attackers from escalating privileges. CNAPPs detect unusual cloud activity, like the use of excessive permissions or lateral movement, creating more complete coverage and visibility into these events.

The Future of AI in Cloud Security

AI-powered threat detection is evolving. As cyberattacks get more sophisticated, so do machine learning algorithms and the mitigation abilities of AI systems. While AI’s benefits today include real-time detection and remediation, the future will bring predictive defense models and even self-healing systems. We predict:

1. Quantum computing will disrupt encryption and threat detection

That will render many of today’s security measures obsolete. It will also change how AI threat detection operates. For instance, attackers using quantum-powered algorithms could bypass AI models more effectively, manipulating AI to overlook its actions.

Teams will need new algorithms that are resistant to quantum attacks.

2. AI will help systems become self-healing and self-adapting

Alert fatigue is out the window in a future with autonomous Security Operations Centers (SOCs) with automatic attack responses, systems that heal themselves from attacks, and adaptive models that detect, but also neutralize, attacks.

Teams will need explainable models to justify automated responses and rely on AI as a force multiplier, not a human replacement. Deeper attack analysis and analysis of automated responses will be core projects for teams.

3. AI threat actors will be tougher to identify

AI is already helping attackers scale faster and bigger, for sophisticated attacks with fewer resources than before. They’ll be able to engineer better AI-generated phishing and social engineering attacks that are harder for employees to detect. The result? Malicious AI could automate social engineering writ large, affecting every aspect of a business (and its public trust). 

Teams will need email security and AI models that detect the most sophisticated manipulation at the video, voice, and text levels.

4. AI Security will come to edge computing

As companies expand cloud adoption and edge computing, security too must expand beyond centralized data centers into distributed environments where the attack surface is also scaling. AI security models will need to detect threats at the edge, in IoT devices, remote sensors, and mobile networks, for example. 

Teams will use AI models trained on multiple decentralized environments, not a single model.

5. AI will link to blockchain for secure identity and dataset integrity

Using blockchain for immutable secure records, AI model verification and identity management is one way that organizations are side-stepping the threat from AI attackers. AI security tools will increasingly use blockchain to log and verify AI security decisions and to prevent the manipulation of models. Decentralized identity security is another approach to moving past the weaknesses of passwords and credential-based security.

Teams will incorporate tools with distributed trust models for more tamper-proof logs and verification mechanisms.

Upwind uses machine learning to detect anomalies faster

Upwind also correlates unusual user behavior with cloud vulnerabilities, so teams not only get instant visibility into potential attacks, but also understand how to prevent them in the future. Machine learning powers a baseline for cloud activities, networks, and application flows. That means that even unknown attack patterns get on teams’ radar faster, for quicker escalation and remediation when it counts.

Want to see how AI can detect and correlate threats? Schedule a demo today.

FAQ

What is generative AI in cybersecurity?

Generative AI in cybersecurity refers to the models that create or modify content, code, and strategy for defensive and offensive security reasons. For defensive cybersecurity, it includes:

• AI-augmented threat detection, using learning models to detect anomalies, as Upwind does with behavioral analysis, finding patterns in typical vs atypical use of resources and flagging unusual behavior.
• Automating incident response, generating real-time response strategies when attacks are detected, as in a SOAR platform with AI that can generate automated workflows based on past incidents.
• AI-assisted code and security policy generation, with generative AI helping developers write more secure code by automatically suggesting best practices in the build phase.
• Phishing and AI deepfake detection, with AI models trained to detect AI-generated phishing emails, images, etc.

For attackers, generative AI is used to engineer phishing emails, write chat messages, fake audio and video, create self-mutating malware that evades detection, automate scanning for vulnerabilities to exploit, and create disinformation communications at scale.

What are the benefits of using generative AI in cybersecurity?

Generative AI can help cybersecurity teams in the following ways:

• Faster threat detection: Scanning logs, events, and anomalies at scale in real time.
• Automated incident response: Generating real-time remediation steps.
• AI-assisted threat intelligence: Analyzing attack patterns to identify new threats and prevent future ones.
• Phishing and deepfake defense: Identifying AI-generated impersonations, images, and scams.
• Secure code generation: Helping developers identify vulnerabilities and suggesting fixes early in the CI/CD pipeline.

In the case of faster threat detection and automated incident response, generative AI can provide summarized suggestions and reports, as Upwind’s insights guide teams through attack paths and remediation efforts. 

How effective is AI in detecting and preventing cyber threats?

AI is highly effective at detecting and preventing cyber threats. First, it can process billions of pieces of data in real time, so it’s invariably faster than human analysts. But its effectiveness depends on human intervention and the support it gets from other tools and data sources: implementation, data quality, and the use of AI by attackers can reduce its effectiveness.

Some studies have suggested that 80% of teams believe AI has helped them identify hidden threats they wouldn’t otherwise discover at all. The strengths of AI in threat detection include speedy threat detection, behavioral analysis, automated response, and threat intelligence correlation. For teams that face challenges in any of those areas, AI can be a game changer.