What is AI in cybersecurity?

Cybersecurity threats are more prevalent and sophisticated than ever. Cybercriminals and hackers are continually innovating, using ransomware-as-a-service and phishing-as-a-service tools to steal valuable data and put your business — and customers — at risk. In fact, the global cost of cybercrime is expected to reach $10.5 trillion annually by 2025.

One way to stop these threats in their tracks? With an even more sophisticated tool: artificial intelligence (AI). AI has emerged as a game-changing force, offering unparalleled capabilities to preemptively identify and neutralize threats. Let’s take a closer look at how to use AI in cybersecurity and harness its revolutionary protective abilities.

What is AI in cybersecurity?

AI in cybersecurity refers to the application of technologies like machine learning, neural networks, and advanced data analysis to enhance security protocols. These technologies empower systems to autonomously detect, analyze, and respond to threats with unprecedented speed and accuracy.

• Machine learning security: This involves training algorithms on historical data to recognize patterns associated with cyber threats, enabling predictive capabilities.
• Neural networks: These systems process and identify complex patterns in massive datasets, providing insights that traditional systems can’t achieve.
• Behavioral analysis: AI monitors regular user and system behavior, flagging anomalies that could signify malicious activity.

In 2023 alone, 41 percent of small businesses fell victim to a cyber threat in the U.S. — a rise from both 2022 and 2021. And these attacks come at a high cost, with said businesses paying over $16,000 in cyber ransoms over the past 12 months. 

How is AI cybersecurity different from traditional cybersecurity?

AI-driven cybersecurity distinguishes itself from traditional methods through its dynamic adaptability and real-time threat mitigation capabilities. Here are the key differences:

• Proactive defense: Traditional firewalls and antivirus programs rely on predefined rules, making them less effective against novel threats. AI, however, adapts to new attack patterns as they emerge for a more proactive line of defense.
• Massive data processing: Traditional methods often struggle to analyze the sheer volume of data that modern networks generate. AI excels at processing and interpreting vast amounts of data in real time, ensuring faster threat detection.
• Zero-day attack protection: AI’s ability to identify behavioral anomalies helps organizations detect and counter zero-day vulnerabilities before attackers exploit them.

Through these efficiencies and threat detection abilities, it’s projected that AI will save businesses $10.4 billion in cybersecurity costs by 2027, in comparison to traditional rule-based intrusion detection systems.

Why is AI in cybersecurity important?

AI has become a necessity in cybersecurity for several compelling reasons. As threats grow more sophisticated, it offers a robust defense mechanism that adapts to new challenges. Here’s how:

• Protection against sophisticated threats: Modern cyberattacks are increasingly complex and difficult to detect using traditional methods. AI’s predictive and adaptive capabilities address these challenges effectively.
• Reducing manual overhead: Cybersecurity teams often face alert fatigue because of the volume of potential threats flagged by traditional systems. AI reduces this burden by automating threat prioritization and response.
• Scalable solutions: AI systems offer scalable security solutions tailored to the needs of organizations of all sizes, from small startups to global enterprises.

• Proactive threat detection: AI can detect threats before they cause damage, ensuring a stronger line of defense and fewer costly breaches.
• Improved accuracy: AI cybersecurity software minimizes false positives, reducing unnecessary disruptions to workflows (although false positives can also be a challenge — more on this later).
• Enhanced data protection: AI protects sensitive data against breaches through real-time monitoring and anomaly detection, keeping the data of both your business and its customers safe.

Nowadays, AI-driven cybersecurity isn’t just a nice-to-have — it’s a necessary tool in your arsenal. In fact, 70 percent of organizations report that AI has helped them detect threats that would previously have gone unnoticed. So if your organization fails to keep up with cybercriminals, you could become a prime target.

How AI enhances cybersecurity

The AI in the cybersecurity market is set to grow from $22.4 billion in 2023 to $60.6 billion by 2028, driven by increasing adoption across industries. So how exactly does AI enhance cybersecurity? 

1. Real-time threat detection

• Use case: AI continuously monitors network activity, identifying and responding to threats as they happen.
• Example: An AI-powered system might detect an unusual spike in outbound traffic, signaling a potential data exfiltration attempt.

2. Automated response systems

• Use case: If a threat is identified, AI can take immediate action to minimize its impact.
• Example: AI can isolate affected systems, block malicious IP addresses, or trigger automated incident response protocols. 

3. Anomaly detection in large datasets

• Use case: AI excels at analyzing massive datasets to identify deviations from expected behavior. 
• Example: It can detect unauthorized access attempts in a corporate cloud environment, even if these attempts mimic legitimate user activity.

4. Enhanced threat intelligence

• Use case: AI gathers and synthesizes data from multiple sources, including threat intelligence feeds, the dark web, and internal network logs.
• Example: By continuously updating its knowledge base, AI can stay ahead of emerging threats. 

6 challenges of using AI in cybersecurity

Despite its advantages, implementing AI in cybersecurity isn’t without its challenges. Organizations must navigate these obstacles to maximize its effectiveness:

1. False positives: AI systems can sometimes misinterpret benign activities as threats, leading to unnecessary disruptions.
2. Data dependency: AI models require large, diverse datasets to function effectively. Poor-quality data can compromise their accuracy.
3. Adversarial AI risks: Attackers may exploit AI systems by feeding them misleading inputs — a tactic known as adversarial machine learning. According to Gartner, 41% of organizations reported experiencing cybersecurity incidents which included adversarial attacks of machine learning models.
4. High implementation costs: Developing and maintaining AI-driven systems can be financially prohibitive, particularly for smaller businesses.
5. Ethical concerns: Balancing AI’s automation capabilities with user privacy and ethical considerations remains a critical challenge.
6. Need for human oversight: Organizations must hire or train security professionals to implement, oversee, and update these systems in order to mitigate challenges — something that can be costly and time-consuming.

Photo by Beyzanur K.