Security teams don't need more alerts—they need more accurate ones. But most traditional tools still rely on signatures and rules, which means they often miss real threats sitting in plain sight.
The solution is machine learning. For security teams, it means less noise, more automation, and sharper detection. ML algorithms scan activity in real time and prioritize risks, allowing teams to catch threats early—whether it’s a developer pushing code at 3 a.m. from an unfamiliar IP or a sudden spike in outbound traffic. Rule-based systems would likely miss both scenarios, but machine learning doesn’t.
In this guide, we'll explain how machine learning works in cybersecurity, where it's already making a difference, and the challenges that come with it.
What Is Machine Learning?
Machine learning is a branch of AI that enables systems to learn from data instead of relying on hard-coded rules. ML models run algorithms across massive datasets to find patterns and refine their predictions as they process new information. Over time, they improve their ability to detect anomalies that humans or traditional tools might miss.
For cybersecurity teams, that’s a big deal. According to IBM's 2023 “Cost of a Data Breach” report, organizations still take an average of 277 days—roughly nine months—to spot and contain a breach. That’s a huge window for attackers to exploit vulnerabilities and cause major damage.
With machine learning, detection happens much faster. ML algorithms continuously scan activity and flag suspicious behavior in real time, sometimes reducing that nine-month timeframe to just weeks or even days. Instead of waiting for a known malware signature to appear, they surface early signs of cyber threats and give defenders a chance to stop attacks before they become full-blown incidents with heavy consequences.
What Is the Relationship Between Machine Learning and Cybersecurity?
In cybersecurity, attackers only need one lucky break. But defenders have to stop every single attempt, a challenge they struggle to meet using traditional security tools. Machine learning gives security teams an edge by analyzing security-specific data like user behavior, network traffic, file transfers, and system activity. With this input, ML algorithms can identify early signs of foul play faster and with greater accuracy before they escalate into full-blown breaches. Thanks to this real-time detection, teams can respond proactively, strengthening their overall defense against evolving threats.
Types of Machine Learning
Security teams use different machine learning approaches based on their goals and the type of data that’s available. Here are the main ML models used to detect and respond to cyber threats.
Supervised Learning
Security teams train supervised learning models by showing them thousands of examples, such as "this email is spam," "this file contains malware," and "this network signature indicates an attack." With time, the model learns to recognize similar patterns in new datasets. It’s an effective strategy, but it requires gathering enough high-quality data to keep improving accuracy and avoid false positives.
Unsupervised Learning for Anomaly Detection
Sloppy attackers don’t bother hiding their work. The pros do. They maintain their cover by making their activity look normal. Unsupervised learning models establish a baseline for typical network behavior and trigger alerts when anything deviates from the norm. There’s no need to teach it what’s “bad”—it simply recognizes when something’s off. And that can signal a cyberattack or other vulnerabilities.
Semi-Supervised Learning
For some organizations, it’s not uncommon for thousands of phishing emails to arrive daily. But most security teams have the bandwidth to review a few dozen. Semi-supervised learning uses small sets of verified examples to find patterns in unlabeled data. This allows the model to make informed predictions, essentially teaching itself from a mix of known threats and unclassified traffic.
Reinforcement Learning
Traditional security systems need human updates whenever threats change. Reinforcement learning is different—it improves by learning from its own mistakes. If it blocks too many legitimate users, it adjusts to be less restrictive. If it misses a real attack, it becomes more sensitive. This self-correcting ability makes it useful for fast-changing environments where manual rule updates can't keep up.
4 Benefits of Machine Learning for Cybersecurity
Security teams often feel like they’re drowning in alerts while real threats slip past unnoticed. Machine learning in cybersecurity acts as a lifeline, cutting through the noise and handling the scale and complexity that overwhelm human analysts.
Here’s a closer look at how ML algorithms strengthen modern cyber defenses.
1. Detects Hidden Threats in Network Activity
Sometimes, networks show suspicious activity, like repeated login attempts outside business hours, abnormally large data transfers, or sudden spikes in application usage. Machine learning automatically spots these anomalies and flags potential threats—even if the attack method is new.
2. Automates Repetitive Tasks
Alert fatigue is real. Analysts face thousands of notifications daily, and most require no action. Machine learning sifts through them automatically, escalating only the alerts that really need human attention. This allows teams to focus on serious cyber threats instead of wasting time attending to endless queues.
3. Improves Detection Accuracy
False positives eat up time and create blind spots. ML models reduce these mistakes by learning your environment rather than depending on generic rules. They distinguish between legitimate unusual behavior and genuine threats, reducing false positives while catching more real attacks.
4. Speeds Up Incident Response
When an attack is active, every second counts. Machine learning can immediately isolate compromised systems, block suspicious network traffic, and trigger incident response processes. By automating these first steps, security teams have breathing room to investigate and contain the threat before it spreads.
Use Cases and Applications of Machine Learning in Cybersecurity
Security teams use AI and machine learning in cybersecurity to solve challenges that traditional tools can’t. Here's where machine learning makes the most difference.
Assisting Analyst-Led Investigations
Tracing how attackers moved through systems often takes days of manual log review. ML algorithms automatically surface connections between events and highlight the paths that matter most. This reduces investigation time from days to hours, helping analysts detect and understand cyberattacks faster.
Preventing DDoS Attacks
DDoS attacks can overwhelm infrastructure in minutes by flooding systems with massive traffic volumes. Machine learning models recognize these patterns early and block malicious traffic before it compromises systems.
Flagging Unusual Network Activity
Networks generate thousands of daily logs where suspicious activity often blends in with normal operations. ML tools learn what typical behavior looks like and flag unusual activity so it doesn't get lost in all the noise.
Improving Phishing Detection
Modern phishing emails often mimic legitimate communication styles to bypass traditional keyword-based filters. Machine learning examines things like writing style, role-based permissions, and timing of requests to spot fraudulent messages that might otherwise seem legitimate.
Security Challenges With AI and Machine Learning
Machine learning strengthens cybersecurity defenses, but it also introduces new attack vectors that security teams must address. Here are three major risks that information security systems with machine learning capabilities face:
- Adversarial attacks can fool ML models: Attackers can tweak malicious code just enough to slip past detection algorithms. For example, malware might change a few bytes of data to avoid triggering alerts while still carrying out harmful actions.
- Data poisoning undermines accuracy: Bad actors can also corrupt the data used to train ML models. When attackers feed false information into these datasets, they can make machine learning threat detection unreliable, teaching systems to ignore real threats or flag safe activities as suspicious.
- Models need constant tuning and oversight: Cyber threats are always evolving, but many ML models don't automatically adapt. Without continuous updates and monitoring, these systems can miss new attack methods that weren't part of the original training data.
Protect Your Systems With Legit Security's AI-SPM
Development teams rely on AI tools throughout modern software pipelines, but many organizations are unaware of where or how they're applied. Legit Security's AI-enhanced ASPM platform helps teams identify AI usage across DevOps workflows and understand its security implications.
The platform uses machine learning to detect threats accurately while reducing false positives that waste analysts’ time. Whether you need to locate risky AI models already active in your environment or strengthen your overall security posture, Legit provides practical solutions for addressing real-world problems.
Request a demo today
to discover how Legit secures software, from development to production.