AI in cybersecurity – Everything you need to know

There was a time when artificial intelligence (AI) and machine learning (ML) were the exclusive domain of tech giants and forward-thinking organizations. Today, they have become integral parts of many industries — from healthcare to finance to even everyday consumer products.

As AI has become more accessible, its influence has also expanded into cybersecurity, where it's reshaping how we tackle evolving threats. This post will explore the evolution, use cases, advantages, disadvantages and future trends of AI in cybersecurity.

Let’s start by examining AI’s evolving role in the cybersecurity world:

The machine learning era

The journey started with machine learning. Early cybersecurity tools applied machine learning to detect patterns in network traffic and identify potential threats. However, these systems were limited in their ability to handle complex threats and large datasets. They also struggled with identifying new, unknown threats. This stage laid the foundation for smarter systems that could learn from the data over time and adapt to emerging risks.

The dawn of deep learning

Deep learning built on machine learning by introducing neural networks that could process large amounts of unstructured data. This enabled systems to go beyond traditional malware detection and become more effective at analyzing behaviors and network traffic. Deep-learning-powered AI systems showed exceptional accuracy in identifying sophisticated attacks like advanced persistent threats (APTs).

Generative AI and the democratization of AI in cybersecurity

The latest evolution, which has also democratized the use of AI in cybersecurity, is generative AI. It can not only detect threats but also simulate and anticipate new attack vectors by creating synthetic data and scenarios. While powerful, generative AI also poses risks, as attackers can use it to create more sophisticated cyberattacks.

Next, let’s discuss the main ways in which AI is beneficial for cybersecurity:

Identity and access management (IAM)

IAM systems can become much smarter by incorporating AI. For example, AI can identify if an employee is trying to access sensitive data at unusual times or from an unexpected location and can flag it for further investigation. While legacy access management systems can do this based on pre-set rules and with rigid geofencing, modern IAM tools offer smart risk scoring and a flexible approach to minimize user friction.

Threat and anomaly detection, and risk assessment

AI helps organizations detect potential threats and anomalies more proactively than traditional systems. Machine learning algorithms analyze network traffic, user behavior and past incidents to spot unusual patterns that could indicate an attack. For example, an AI-powered security system can detect malware variants or phishing attacks that evade traditional, signature-based detection methods.

Human-AI collaboration

AI augments human decision-making in cybersecurity by analyzing vast amounts of data that would be impossible for humans to process quickly. For example, AI can filter out false positives and present only the most critical alerts, so that human experts can focus on the most pressing threats. This reduces burnout by enabling security intelligence teams to reclaim valuable time and resources.

Generative AI for defense, attack and governance

Generative AI can be used to model how new forms of ransomware or cyberthreats could spread through a system. For example, security teams may model lateral movement attacks to identify network vulnerabilities that could allow attackers to move from one system to another. Additionally, generative AI also helps in formulating new cybersecurity policies and in ensuring compliance with evolving standards.

Automation, recommendations and guidance

AI might help automate many cybersecurity tasks, such as patch management, threat prioritization, incident response and vulnerability scanning. Modern AI cybersecurity solutions also provide actionable and contextualized recommendations for mitigating risks. For example, they can suggest specific security measures, such as firewall rules, endpoint security controls or intrusion detection system configurations to address identified vulnerabilities.

Here are some additional, tangible benefits of using AI to beef up your security:

• Predictive capabilities: AI can predict and anticipate future threats by learning from past attacks and detecting patterns that indicate upcoming risks. This enables you to implement proactive defense strategies.
• Improved incident response: AI can automate and speed up responses to detected threats, such as isolating compromised devices or blocking malicious activity.
• Enhanced accuracy: Machine learning and deep learning models can identify complex, sophisticated attacks that are often missed by traditional rule-based systems. For example, they may detect unusual patterns in email traffic or user behavior that indicate phishing scams or social engineering attempts.
• 24/7 monitoring: AI systems can work around the clock to provide constant monitoring, without the need for breaks or shifts. This improves overall security coverage and empowers your teams to focus on more creative or enjoyable tasks.
• Adaptive learning: Advanced AI systems continuously improve and evolve, learning from new threats and adapting their defense strategies without needing constant manual updates.
• Bot vs. bot battles: As hackers increasingly use AI-powered bots to launch attacks, manually defending against them becomes nearly impossible. These bots can carry out sophisticated and high-speed attacks, such as automated phishing campaigns or network intrusions. To counteract this, AI-powered cybersecurity bots can be deployed to fight back in real-time. These bots can identify malicious activity, adapt to evolving attack methods and respond instantly to neutralize threats before they cause damage.

Even though AI offers numerous undeniable advantages in cybersecurity, it's important to also acknowledge its potential drawbacks.

• High implementation costs: It can be expensive to set up AI-powered cybersecurity systems, as they often require significant investment in technology, infrastructure and skilled personnel.
• Over-reliance on AI: Relying too much on AI can lead to complacency, where security teams may trust AI systems blindly and overlook potential gaps in protection.
• Vulnerability to adversarial attacks: Hackers can use AI against AI by creating adversarial inputs—manipulated data that tricks AI models into making incorrect decisions or missing a threat.
• Bias and false negatives: AI models are only as good as the data that has trained them. If the data is biased or incomplete, the AI system may produce biased outcomes or miss certain cyberthreats entirely. Current iterations also miss accuracy benchmarks, which means that any AI-based tool can only complement, not replace, existing robust defenses.
• Lack of human judgment: While AI is excellent at processing data and estimating optimal plans of action, it still lacks human intuition and judgment. There are still plenty of situations where AI fails to account for unique or context-specific security risks.

The future of AI in cybersecurity is likely to bring even more advanced, proactive and automated defenses. Here’s what can be expected:

AI-enabled SIEM/SOAR tools

Security information and event management (SIEM) tools will become increasingly powered by AI, not only detecting threats but also taking steps to protect systems and minimize damage. These systems will be able to identify anomalies, isolate threats, reconfigure defenses, assess risks and automatically apply patches without human intervention.

Increased use of quantum computing

With the rise of quantum computing, AI systems will be able to process data and identify threats at unprecedented speeds. This will lead to breakthroughs in encryption methods and more secure communication channels.

AI collaboration with humans

While AI will become more powerful, human-AI collaboration will continue to be vital. AI will handle large-scale data analysis and automated defenses, while human experts will focus on high-level decision making to ensure a balanced approach to security.

Adaptive and predictive threat models

AI will become better at predicting future threats by learning from global cybersecurity incidents. It will anticipate attack trends and prepare systems to defend against emerging tactics before they become widespread.