AI or ML? What’s the Difference?

Originally Published June 23, 2025

What Is Artificial Intelligence?

Artificial intelligence refers to the broader concept of machines performing tasks that typically require human intelligence. This includes analyzing information, recognizing speech, making decisions, solving problems, and generating content. AI is not a single tool or system. It is a category of capability that spans everything from the spam filter in your email to the autonomous systems managing factory floors.

In cybersecurity, AI can sort through enormous volumes of data, recognize potential threats, and automate responses that previously required a human team. An AI system monitoring your network might flag suspicious behavior within seconds of it appearing, correlating signals across dozens of systems simultaneously. A human analyst working alone couldn't match that speed or scale.

What Is Machine Learning?

Machine learning is a subset of AI, but it works differently from rule-based systems. Instead of following a fixed set of programmed instructions, ML systems learn by processing data. The more data they see, the better they get at identifying patterns, predicting outcomes, and detecting behavior that falls outside the norm.

In practice, machine learning powers the tools that detect unusual login activity, identify phishing attempts, or flag changes in system behavior that could signal an intrusion. These systems establish a baseline of what normal looks like on your network, then alert on deviations. They can catch threats faster than a human analyst, with fewer false alarms, and without needing someone to manually write a rule for every new attack type.

That last point matters. Traditional security tools are signature-based: they look for known bad patterns. ML-based tools can identify threats they have never seen before by recognizing that something doesn't look right, even if it doesn't match a known signature.

How Deep Learning and Neural Networks Fit In

Deep learning is a more advanced type of machine learning that uses layered networks, often called neural networks, to process large volumes of complex data. These networks can identify patterns without being explicitly told what to look for, making them effective for tasks that involve unstructured data: images, audio, video, and natural language.

In cybersecurity, deep learning powers some of the most capable detection systems available. It enables analysis of network traffic at packet level, identification of malicious code embedded in files, and detection of voice or video patterns associated with deepfake fraud. It is also the technology behind the generative AI tools attackers are now using to create convincing phishing emails and fabricate executive identities in real-time video calls.

Understanding deep learning matters for business leaders because it explains why modern threats are harder to catch with older tools, and why the detection gap between AI-equipped security operations and those without continues to widen.

Why the Distinction Matters for Your Business

When a vendor says their product uses AI, that tells you very little. The meaningful questions are which techniques it uses, what data it learns from, how it handles false positives, and whether it adapts to your specific environment. A rule-based system marketed as AI is not the same as a system that uses behavioral ML to detect novel threats in real time.

IBM's 2025 Cost of a Data Breach Report makes the business case plainly. Companies using AI and automation extensively in their security operations saved nearly $1.9 million per breach compared to those with no AI use, and identified breaches 80 days faster. The global average breach cost fell to $4.44 million in 2025, the first decline in five years, driven largely by AI-powered detection and containment. U.S. businesses, however, saw costs move in the opposite direction, reaching a record $10.22 million average.

The same report found that shadow AI, employees using unapproved AI tools without IT oversight, added an average of $670,000 to breach costs. Firms with high shadow AI exposure took longer to detect incidents and faced broader data compromise. That finding connects directly to governance. The benefits of AI in your security stack are real, but only if your AI use across the business is managed.

AI and ML Working Together in Cybersecurity

In a modern security operation, AI and ML work in combination rather than independently. ML models analyze network telemetry and endpoint behavior to surface anomalies. AI-powered systems correlate those anomalies across the environment, prioritize alerts, and trigger automated responses. Deep learning models handle the more complex pattern recognition tasks: identifying malicious code variants, detecting deepfake media, and flagging credential abuse that signature tools miss.

The result is a security operation that can process more signals, respond faster, and catch more sophisticated attacks than a team relying on traditional tools. According to research cited by Total Assure, one transportation manufacturer reduced its attack response time from three weeks to 19 minutes after implementing AI automation, a reduction of more than 99%. That kind of improvement removes manual bottlenecks that let attackers move laterally through a network while analysts are still triaging alerts.

For a deeper look at how AI-powered tools are being deployed against businesses right now, see our post on how AI is being used to wage war on businesses.

Vendor Security Evaluation

Every security vendor claims to use AI. Knowing the difference between AI and ML gives you better questions to ask. Find out whether the product uses static rules or adaptive models. Ask how the system handles threats it hasn't seen before. Ask how it learns from your specific environment over time, and how false positive rates are managed. A system that generates constant noise trains your team to ignore alerts, which is its own category of risk.

Also ask about governance. AI security tools that process sensitive data need the same oversight as any other system handling that data. Access controls, audit logging, and data retention policies apply to your security stack the same as they apply to everything else.

Our AI Security Checklist is a practical starting point for evaluating AI tools across your business, including security applications. The AI FAQs for Business covers the foundational questions leadership teams are working through as adoption accelerates.

Frequently Asked Questions

What's the difference between AI and machine learning in simple terms?

AI is the broad concept of machines performing tasks that require human-like intelligence. Machine learning is one specific technique within AI, where systems improve their performance by learning from data rather than following fixed rules. All machine learning is AI, but not all AI uses machine learning.

How is machine learning used in cybersecurity specifically?

ML is used to detect anomalous behavior on networks and endpoints, identify phishing attempts, flag unusual login patterns, and prioritize security alerts. Because ML models learn from your environment over time, they become better calibrated to what normal looks like for your business, which reduces false positives and improves detection accuracy.

Are attackers also using AI and machine learning?

Yes. IBM's 2025 Cost of a Data Breach Report found that AI was involved in one in six breaches, primarily through phishing campaigns and deepfakes. Attackers use generative AI to craft convincing phishing emails at scale, clone executive voices for fraud, and develop malware that adapts to evade detection tools. The same technologies defending networks are being used against them.

Does using AI security tools actually reduce breach costs?

According to IBM's 2025 report, yes. Organizations using AI security tools extensively saved nearly $1.9 million per breach on average and detected incidents 80 days faster compared to organizations without AI in their security stack. The global average breach cost fell for the first time in five years, driven largely by AI-powered detection and containment.

What is shadow AI and why does it increase breach costs?

Shadow AI refers to employees using AI tools without IT approval or oversight. IBM's 2025 report found that shadow AI added an average of $670,000 to breach costs and extended detection timelines. When sensitive business data is entered into unapproved AI platforms, it can be stored externally or exposed in ways existing security controls never anticipated.

Do small businesses need to worry about AI-powered threats?

Yes. AI has lowered the barrier to entry for attackers significantly. Sophisticated phishing campaigns, voice cloning, and automated vulnerability scanning that previously required significant resources or expertise are now accessible to less sophisticated threat actors. Small and midsize businesses are frequently targeted precisely because their defenses often lag behind their larger counterparts.

Related Resources

• Risks of Shadow AI
• How AI Is Waging War on Your Business
• Deepfake Detection Guide
• AI Security Checklist for Businesses
• AI FAQs for Business
• AI in the Workplace: What Every Business Needs to Know
• How Phishing Attacks Work
• STACK AI Hub