How AI can be used for threat detection

Discover how AI can enhance the threat detection capabilities of the SOC team.

In today's complicated threat landscape, AI technology has become an essential tool for security teams in their battle against cyberattacks. By using AI and machine learning, security teams can quickly sort through large amounts of data to find unusual activities in network traffic and carry out responses without human intervention. With threats scaled by AI, defenders need to use the same technology to meet the landscape.
In this article, we will see how AI can enhance the threat detection capabilities of the SOC team and discuss the leading AI-powered security solutions commonly used by enterprises to boost their defenses.

Modernized threat detection

Modern AI systems examine petabytes of logs, link seemingly unrelated events across networks and uncover attack patterns that would take human analysts weeks to find. These systems learn what normal behavior looks like for every user, device and application in an environment. They flag any deviations that may indicate a compromise. Suppose a ransomware strain starts encrypting files or a threat actor moves laterally through a network. In that case, AI-driven security solutions can isolate infected systems, block harmful communications, and notify response teams in seconds.

Organizations now use AI to tackle sophisticated cyberattacks and provide decision-makers with the information they need to stop today's complex threats. Security operation centers (SOC) rely on AI to prioritize alerts, cutting down thousands of daily notifications to a few genuine incidents that need human investigation. Predictive models can forecast attack vectors before they happen, allowing teams to fix vulnerabilities and strengthen defenses in advance. Then using sophisticated investigations platforms, analsysts can safely investigate threats at their source through direct engagement.

AI technology works both ways, and cybercriminals are also using LLMs to create new threats. They develop polymorphic malware that continually changes its signature to avoid detection by systems trained on known patterns. Each infection uses slightly different code, making traditional antivirus signatures ineffective. Attackers harness generative AI to design complicated phishing campaigns that imitate human writing styles, replicate executive communication patterns and produce convincing fake documents that can pass a quick human review.

What is artificial intelligence (AI) in threat detection?

AI threat detection uses a plethora of techniques to detect and respond to cyberthreats. Here are the most common AI techniques used in threat detection:

Machine learning (ML)

ML models are trained on large datasets to distinguish between legitimate and malicious activity. Three main approaches drive security applications:

• Supervised learning: This approach uses labeled training data. Security teams provide the model with thousands of known malware samples marked as "malicious" and legitimate files labeled "benign." The model learns to identify distinguishing features, such as code structures, API calls and file behaviors. It then applies this knowledge to classify new files.
• Unsupervised learning: This one finds patterns and anomalies in data without predefined labels. These models create baselines of regular network traffic, user behavior or system activity and then flag any deviations. This method is particularly good at detecting unknown threats. For instance, if a user account suddenly accesses sensitive databases at 3 AM from an unusual location, unsupervised learning flags this activity as strange, even if there are no prior examples of this specific activity.
• Semi-supervised learning: This one combines both techniques. It uses small amounts of labeled data along with larger sets of unlabeled data. This approach is practical when labeled threat data is complex or expensive to obtain. It allows models to learn from limited examples while recognizing patterns in larger datasets.

Deep learning (DL)

Deep learning technology employs multi-layered neural networks to recognize complex patterns in large-scale datasets. DL is especially effective in detecting zero-day malware. For instance, instead of depending on known signatures, DL models examine the raw code or behavior of a file. By training on millions of both safe and harmful samples, a model learns to identify the coding structures and API calls that signal malware. This enables it to find new malware variants (zero-day threats) and polymorphic malware that alters its code to avoid traditional malware scanners.

Natural language processing (NLP)

NLP allows security systems to analyze human-readable text across various sources, such as phishing emails, chat logs and reports to identify threats, extract intelligence and automate response workflows.

Phishing detection

NLP models review email content to spot phishing attempts. They look at linguistic patterns, urgency cues and impersonation methods. The system searches for alarming phrases such as "verify your account immediately" or "unusual activity detected" combined with grammatical inconsistencies or domain mismatches. 

Malware analysis

Security researchers use NLP to analyze malware code comments, function names and strings found in binaries. By examining the language patterns in these elements, NLP models can classify malware families, detect code reuse across different campaigns and connect samples to specific threat actors. For instance, when a new ransomware strain emerges with Russian-language comments and function names that match known APT groups, NLP aids in attributing the attack to that specific group.

Threat intelligence extraction

NLP processes security blogs, vulnerability reports, dark web forums, and threat feeds to extract indicators of compromise (IOCs) and tactics, techniques and procedures (TTPs). Instead of reading hundreds of reports each day, security teams use NLP to find new exploits, extract CVE numbers, parse IP addresses and domains, and summarize attack methods. For example, when researchers publish an analysis of a zero-day exploit, NLP systems quickly extract the vulnerability details, affected software versions, and recommended fixes.

Insider threat detection

NLP monitors internal communications within an organization, including emails, chat messages, and code repository commits. It looks for signs of malicious intent or policy violations. The system flags employees who discuss data theft, express grievances before leaving or communicate with suspicious external parties, such as the company's competitors. If an employee emails large attachments to personal accounts or uploads them to public cloud while searching for "how to delete audit logs," NLP connects these text signals with unusual behaviors.

Security alert triage

Security information and event management (SIEM) systems generate thousands of alerts daily. NLP can be used to parse alert descriptions, correlate them with threat intelligence and prioritize incidents based on severity and context. Rather than treating every "failed login attempt" equally, NLP distinguishes between a user mistyping their password and coordinated brute-force attacks by analyzing patterns in alert text and associated metadata.

Applications of AI in threat detection

Organizations are rushing to deploy security solutions powered by AI to boost their security posture and strengthen their cyber defenses. Here are the three most widely used AI security solutions in enterprise contexts:

Network security

AI-powered network security solutions monitor digital interactions across the entire organization's IT environment to detect intrusions, data exfiltration, and lateral movement. For instance, ML models establish behavioral baselines for normal network activity — typical data transfer volumes, communication patterns between systems/users/applications and protocol usage—then flag deviations that signal potential malicious activity. 

For example, when an employee's laptop suddenly initiates outbound connections to command-and-control servers in foreign countries, AI systems correlate this with unusual DNS queries, encrypted traffic to suspicious domains and abnormal data upload volumes. The system can act automatically by blocking the connection, isolating the affected device, and alerting security teams before sensitive data leaves the network.

AI can also identify distributed denial-of-service (DDoS) attacks by analyzing traffic patterns in real-time. Instead of waiting for manual threshold triggers, ML models recognize the subtle signatures of coordinated bot traffic, distinguishing legitimate traffic spikes from malicious floods. Deep packet inspection powered by AI examines payload content to detect hidden malware communications, even when attackers use encrypted channels or steganography to hide their activities.

Endpoint security

AI-powered endpoint detection and response (EDR) solutions monitor individual computing devices like laptops, servers, mobile phones for malicious activity. These systems track process execution, file modifications, registry changes and memory usage to identify threats that bypass traditional antivirus software.
Behavioral analysis engines watch how applications interact with system resources. When ransomware begins encrypting files, AI detects the rapid succession of file modifications, unusual CPU cycles and attempts to delete shadow copies (this is a characteristic that distinguishes encryption malware from legitimate backup software). The system can halt the process, quarantine the compromised device, and restore encrypted files before important damage occurs.

An AI-powered endpoint security solution also identifies fileless malware that operates entirely in memory without touching the disk drive. By monitoring PowerShell commands, script execution and in-memory injection techniques, ML models detect attackers living off the land with legitimate system tools. When an attacker uses Windows Management Instrumentation (WMI) to execute commands remotely or launches encoded PowerShell scripts to download payloads, AI flags these tactics even though no malicious file exists.

Fraud detection

Financial institutions and e-commerce platforms use AI to spot fraudulent transactions, account takeovers, and payment abuse in real time. ML models look at transaction patterns, such as purchase amounts, geographic locations, device fingerprints and timing, to tell the difference between legitimate customers and criminals.

For example, when a credit card is usually used for small local purchases, but suddenly shows high-value electronics bought from multiple countries in a short time, AI fraud systems flag it as suspicious. The system checks the speed of transactions, geolocation oddities (like buying in New York and Tokyo within an hour), and unusual behaviors (such as purchasing categories the user has never bought before).

AI also finds synthetic identity fraud, where criminals mix real and fake information to create false identities for credit applications. By examining patterns in application data, credit behavior and links across multiple accounts, ML models identify synthetic identities that traditional systems might overlook.

Account takeover detection uses AI to spot when real accounts come under criminal control. The system tracks login patterns, device details, IP addresses and behavior after login. If an account suddenly logs in from a new device, changes contact information and tries to transfer funds or make large purchases — all within minutes — AI blocks the activity and requires extra authentication before the session can continue.

AI is reshaping how security teams detect and respond to threats. Its ability to correlate large datasets, identify hidden attack patterns, and act in real time makes it a critical component of modern defense strategies. Yet, as defenders integrate AI more deeply into SOC operations, adversaries are also weaponizing the same technology to evade detection and automate attacks. The most effective cybersecurity posture will come from combining AI-driven insights with human judgment, ensuring that automation enhances but does not replace analyst expertise.

SOC and cyber teams tracking enterprise-level threats need enterprise-level solutions for their investigations. Request a demo to see how a purpose-built solution like Silo enables collaboration, audit trails and click-to-appear anywhere technology.

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