How Is AI Used in Cybersecurity?

AI cybersecurity is revolutionizing how organizations defend against digital threats. From generative AI powering sophisticated attacks to machine learning strengthening security solutions, understanding how AI is used in cybersecurity has become essential for cybersecurity professionals navigating today’s complex threat landscape

Artificial Intelligence in cybersecurity refers to the application of machine learning, deep learning, natural language processing, and other AI technologies to protect digital systems from cyber threats. AI security tools analyze vast amounts of data, identify patterns, and automate responses at speeds impossible for human analysts to match.

AI plays a dual role in today’s security landscape. On one hand, it enhances defenses by automating threat detection, analyzing massive datasets, and identifying patterns too subtle for human analysts. On the other, cybercriminals are leveraging generative AI to automate attacks, create sophisticated malware, and evade detection systems. This dual-use nature highlights the critical importance of understanding how AI shapes both offensive and defensive cybersecurity strategies.

The applications of AI in cybersecurity have expanded dramatically. According to SNS Insider research, the generative AI cybersecurity market is expected to grow from $7.73 billion in 2025 to nearly $80 billion by 2033. Here’s how cybersecurity teams are leveraging AI across key security domains:

AI-powered threat intelligence platforms represent a paradigm shift in how security teams identify and respond to cyber threats. Machine learning algorithms analyze network traffic patterns, user behavior, and system logs to detect anomalies that could indicate an attack. For example, Darktrace’s Enterprise Immune System uses machine learning to autonomously monitor network activity, having successfully identified zero-day ransomware attacks within minutes.

In 2025, agentic AI has emerged as the next generation of threat intelligence, giving defenders the speed and autonomy to predict and respond to threats across the full attack lifecycle. According to Cyble’s threat intelligence research, unlike conventional AI that simply analyzes data, agentic AI evaluates scenarios, prioritizes risks, and initiates responses with human-like judgment at machine speed.

AI transforms security operations by automating routine tasks and accelerating incident response. Next-generation SOC tools powered by AI can generate incident summaries from complex log data, accelerate investigations, and provide contextual insights that help security teams make faster, more accurate decisions. Google’s AI systems, for instance, block over 100 million phishing attempts daily by scanning email patterns and flagging suspicious behavior in real time.

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AI-based network security solutions use autoencoders and anomaly detection algorithms to identify suspicious network traffic with unprecedented accuracy. These systems employ data pre-processing methods and reconstruction error functions to detect threats that traditional rule-based Network Intrusion Detection Systems would miss. Machine learning models continuously learn from network behavior, adapting to new attack vectors as they emerge.

As organizations migrate to cloud environments, AI-powered cloud security solutions have become essential. According to Microsoft’s security research, cloud-native application protection platforms (CNAPP) use AI to unify security tools like cloud security posture management (CSPM), cloud infrastructure entitlement management (CIEM), and cloud workload protection platform (CWPP). These integrated platforms correlate signals across applications, infrastructure, and user behavior to identify threats in complex multi-cloud environments.

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While AI strengthens defenses, cybercriminals are weaponizing the same technologies to launch more sophisticated attacks.

Generative AI has fundamentally changed the threat landscape. Criminals use these tools to automate reconnaissance, craft deepfakes, and adapt attacks in real time. Key threats include:

• AI-enhanced phishing: According to DeepStrike security research, phishing attacks have surged 1,265% since the advent of generative AI tools. AI-generated emails now mimic tone, logos, and context with near-perfect accuracy, bypassing legacy security filters.
• Deepfake fraud: Netwoven’s State of AI Identity Threats 2025 report found over 2,000 verified deepfake incidents targeted businesses in Q3 2025 alone. The World Economic Forum reported that criminals used AI-generated video clones of Arup’s senior executives to trick an employee into transferring $25 million.
• Polymorphic malware: AI enables malware that automatically modifies its code to evade detection. DeepStrike research indicates polymorphic tactics are now present in an estimated 76.4% of all phishing campaigns.
• AI-generated malware: Tools like DeepLocker demonstrate how AI can create malware that remains dormant until it detects a specific target, making it nearly impossible for traditional security tools to identify.

As AI adoption accelerates, organizations need structured approaches to manage associated risks. The NIST AI Risk Management Framework (AI RMF) has emerged as a gold standard for AI governance, providing guidelines to address risks while promoting trustworthy AI development.

The AI risk management framework comprises four core functions:

1. GOVERN: Establishes organizational policies, processes, and accountability structures for AI risk management.
2. MAP: Identifies and categorizes AI systems, their contexts, and potential impacts.
3. MEASURE: Assesses and tracks identified risks using appropriate metrics and testing methods.
4. MANAGE: Implements strategies to mitigate, monitor, and respond to AI-related risks.

NIST released new SP 800-53 Control Overlays specifically designed for securing AI systems, addressing vulnerabilities like prompt injection attacks, model poisoning, and adversarial attacks designed to manipulate AI decision-making.

Mixed sentiment surrounds the application of AI in cybersecurity, and not without reason. We ran a survey with cybersecurity practitioners from across the globe, and the insights reveal a nuanced picture of where AI fits within modern security toolkits.

“AI is not a magic bullet, but a tool to augment human capabilities,” notes one practitioner. While AI can facilitate automation and intelligent analysis necessary for defending against complex cyber threats, the synthesis of AI and human expertise remains crucial. As one respondent captured: “AI provides us with the data, but it’s our job to understand the context and make informed decisions.”

Maintaining data integrity when training AI systems has proven difficult. Key challenges identified by cybersecurity teams include:

• Trust, accountability, and privacy concerns
• Bias in AI algorithms affecting detection accuracy
• Adequacy of training datasets for emerging threats
• Financial costs of developing effective AI security solutions
• Human resource constraints in specialized AI security roles

Cybersecurity practitioners express significant concerns about AI deployment ethics. “How we use AI in monitoring and data analysis must be balanced with respect for individual privacy rights,” explains one respondent. Addressing these concerns requires developing clear legal frameworks, ethical guidelines, and mechanisms for ensuring AI system transparency.

For security teams looking to leverage AI, a strategic approach is essential:

1. Strategic planning: Develop a master plan aligning AI capabilities with organizational security goals. Identify areas where AI can enhance existing measures and set clear integration objectives.
2. Education and training: Invest in AI and cybersecurity training for your security team. Build awareness and skills related to the convergence of AI and cybersecurity to ensure effective tool utilization.
3. Implement security measures: Deploy AI and machine learning algorithms for threat detection while maintaining human oversight. Use advanced detection algorithms and continuous monitoring.
4. Adopt zero trust architecture: Combine zero-trust principles with AI-driven anomaly detection to secure hybrid and multi-cloud environments.
5. Continuous evaluation: Maintain ongoing assessment processes to ensure AI tools remain effective against evolving threats. Update models and algorithms regularly.

OffSec is actively integrating AI across its learning platform to enhance cybersecurity education. Learn more about how OffSec is leveraging AI to transform the learning experience.

OffSec Knowledge Artificial Intelligence (KAI) represents a groundbreaking advance in cybersecurity education. This AI-powered guide is available 24/7 to answer questions and assist learners through their journey. Behind the scenes, KAI leverages natural language processing and machine learning to understand queries and deliver relevant responses, constantly learning and improving to tailor support to individual needs.

OffSec’s Proving Grounds labs and Cyber Ranges provide realistic environments where cybersecurity professionals can practice against the latest threats, including AI-enhanced attack scenarios. When new vulnerabilities emerge, OffSec’s team rapidly integrates them into training environments.

For those building foundational skills, the PEN-200 course teaches essential penetration testing techniques, while advanced practitioners can pursue the OSEP certification for expertise in evasion techniques and breaching modern defenses.

Cybersecurity teams are navigating AI integration with a blend of caution and strategic acumen. While AI offers tremendous potential for threat detection and response, it also introduces new challenges and attack surfaces. Practitioners should prioritize developing secure, transparent, and ethically grounded AI applications while investing in ongoing training and skills development.

The initial investment in AI can be significant, but the potential for enhanced security posture and operational efficiency makes it essential for modern cybersecurity programs. Whether you’re a penetration tester, security analyst, or security operations professional, understanding how AI is used in cybersecurity is now fundamental to career success.Ready to build job-ready cybersecurity skills? Explore OffSec’s cybersecurity certifications and start your journey toward becoming a skilled cybersecurity professional.