Artificial intelligence (AI) in cybersecurity refers to the use of machine learning, generative AI, AI agents, and related technologies to identify, analyze, prevent, and respond to digital security threats.
Cybersecurity teams traditionally rely on rules, signatures, known indicators, and manually reviewed alerts. AI can analyze much larger volumes of information and identify unusual patterns that may be difficult to detect using predefined rules alone.
This makes AI relevant to threat detection, network security, endpoint security, cloud security, data security, identity and access management, vulnerability management, and cybersecurity risk management.
AI has a dual role in cybersecurity. Organizations can use it to improve detection and response, while cybercriminals can use similar technologies to create more convincing phishing campaigns, automate reconnaissance, generate malicious content, and improve social engineering.
The growing use of AI therefore creates both new cybersecurity capabilities and new security challenges.
Why Does AI Matter for Cybersecurity Today?
Modern organizations generate enormous amounts of security information from computers, mobile devices, cloud platforms, applications, networks, and connected systems.
Reviewing every event manually is difficult. AI can help security teams identify patterns, prioritize alerts, and investigate suspicious activity.
For example, an AI-based security system may identify an unusual login, unexpected network traffic, abnormal data access, or a combination of events associated with a possible account compromise.
AI can support:
- Automated threat detection and security monitoring
- Phishing and malware analysis
- Anomaly detection
- Vulnerability identification
- Fraud detection
- Security incident investigation
- Security information and event management (SIEM)
- Identity and access management
- Cloud security monitoring
- Incident response and alert prioritization
- Security analytics and threat intelligence
AI does not replace fundamental cybersecurity controls. Organizations still need strong authentication, access controls, encryption, secure software development, patch management, backups, logging, employee awareness, and incident response procedures.
AI systems can also become targets themselves. Risks include prompt injection, data poisoning, adversarial attacks, model manipulation, information leakage, and attacks against the infrastructure supporting an AI application.
AI in Cybersecurity: Benefits and Risks
| Cybersecurity area | How AI can help | Key limitation |
|---|---|---|
| Threat detection | Identifies unusual activity across large datasets | False positives and missed threats remain possible |
| Phishing detection | Examines language, links, sender behavior and context | Attackers can also use AI to improve phishing |
| Network security | Detects abnormal traffic patterns | Models depend on suitable data |
| Cloud security | Monitors complex cloud environments | Incorrect configuration can create additional risks |
| Endpoint security | Analyzes device activity and suspicious behavior | Requires continuous monitoring and updates |
| Data security | Identifies unusual access or movement of information | Privacy and data-governance requirements apply |
| Incident response | Helps investigate and prioritize alerts | Human verification may still be required |
| AI security | Helps detect attacks against AI applications | AI systems require their own security controls |
The main advantage of AI is its ability to assist with scale, speed, pattern recognition, and automation. Its limitations mean that human oversight and established security practices remain important.
Recent Developments in AI and Cybersecurity
AI cybersecurity developments accelerated during 2025 and 2026 as governments, standards organizations, and cybersecurity researchers focused increasingly on securing AI systems as well as using AI for defense.
On 16 December 2025, the U.S. National Institute of Standards and Technology (NIST) published the initial preliminary draft of its Cybersecurity Framework Profile for Artificial Intelligence (NIST IR 8596). The draft organizes its approach around three areas: securing AI systems, using AI for cyber defense, and addressing AI-enabled cyberattacks.
The European Union Agency for Cybersecurity (ENISA) also continued to examine AI's role in the threat landscape. Its 2025 Threat Landscape, published in October 2025, analyzed 4,875 incidents observed between July 2024 and June 2025. ENISA reported that AI was being used by threat actors to support malicious activity while AI systems themselves were becoming targets.
ENISA's 2026 Threat Landscape, published in September 2026, continued to identify AI as a dual-use technology. The agency reported increasing use of AI by malicious cyber groups and highlighted the expanded attack surface created by AI systems integrated into organizations.
In the United Kingdom, the government published its AI Cyber Security Code of Practice on 31 January 2025. The voluntary framework covers secure design, development, deployment, maintenance, and end-of-life practices for AI systems.
These developments show a broader shift from simply asking how AI can improve cybersecurity toward also asking how AI itself should be secured throughout its lifecycle.
Global Laws, Regulations and Cybersecurity Policies
AI cybersecurity is affected by different laws, regulations, standards, and government guidance around the world. There is currently no single global AI cybersecurity law.
The European Union AI Act is one of the most significant regulatory frameworks. The Act uses a risk-based approach and includes cybersecurity requirements for high-risk AI systems. The regulation specifically recognizes threats such as data poisoning, adversarial attacks, and vulnerabilities in AI systems and their underlying infrastructure.
Several provisions of the EU AI Act became applicable on 2 August 2026. Transparency requirements also began applying from that date. Rules for certain high-risk AI systems are scheduled to apply from 2 December 2027, while high-risk AI systems embedded in regulated products have a later date of 2 August 2028 under the current implementation timeline.
The EU also updated implementation arrangements through the AI Omnibus, which entered into force on 27 July 2026. Among other changes, it extended the timeline for certain high-risk AI requirements.
In the United States, NIST's AI Risk Management Framework (AI RMF) provides a voluntary framework for organizations managing AI risks. The Cyber AI Profile extends the cybersecurity perspective by connecting AI-related risks with the NIST Cybersecurity Framework.
The United Kingdom's AI Cyber Security Code of Practice, published in January 2025, provides voluntary baseline principles for protecting AI systems. It covers areas including threat assessment, infrastructure security, supply-chain security, testing, monitoring, updates, and secure disposal. The UK government is also working toward using the approach as a basis for an international standard through ETSI.
International standards also play an important role. ISO/IEC 42001 provides a management-system framework for organizations that develop or use AI. It addresses areas such as AI governance, risk management, accountability, transparency, and data management.
Organizations operating across multiple countries therefore need to consider the laws applicable to their location, industry, data, AI system, and intended use.
Useful AI Cybersecurity Tools and Resources
Several established frameworks, databases, and security resources can help organizations understand AI security and cybersecurity risk management:
- NIST AI Risk Management Framework (AI RMF): A framework for managing risks associated with AI systems.
- NIST Cybersecurity Framework 2.0: Organizes cybersecurity activities around Govern, Identify, Protect, Detect, Respond, and Recover.
- NIST Cyber AI Profile: Connects AI-related cybersecurity risks with the NIST Cybersecurity Framework.
- MITRE ATLAS: A knowledge base focused on adversarial threats against AI-enabled systems.
- OWASP Top 10 for LLM Applications: Covers common security risks associated with large language model applications.
- ENISA: Publishes European cybersecurity threat intelligence and AI-related security research.
- CISA: Provides U.S. government resources covering cybersecurity and AI security.
- UK NCSC: Publishes guidance for secure AI development and cybersecurity.
- ISO/IEC 42001: Provides an AI management-system framework for organizational governance.
- SIEM platforms: Centralize security logs and alerts for investigation and monitoring.
- Vulnerability assessment tools: Help identify weaknesses in applications, systems, and infrastructure.
- AI red-team testing tools: Help evaluate AI applications against adversarial inputs and security weaknesses.
These resources should be selected according to the organization's technology environment, risk profile, regulatory requirements, and security objectives.
Frequently Asked Questions
How is AI used in cybersecurity?
AI can analyze security information, identify unusual behavior, detect potential threats, prioritize alerts, investigate incidents, and support automated responses. It can assist cybersecurity teams but does not remove the need for human oversight.
Can AI create new cybersecurity risks?
Yes. AI systems can be exposed to prompt injection, data poisoning, adversarial attacks, model manipulation, information leakage, and other threats. AI can also help attackers scale phishing, social engineering, reconnaissance, and other activities.
What is AI security?
AI security focuses on protecting AI models, applications, data, interfaces, and supporting infrastructure from attacks, manipulation, unauthorized access, or misuse.
Are there global laws governing AI cybersecurity?
There is no single worldwide AI cybersecurity law. Different jurisdictions have introduced laws, regulations, standards, and voluntary frameworks. The EU AI Act, U.S. NIST frameworks, UK AI Cyber Security Code of Practice, and international ISO standards are examples of the approaches currently shaping AI governance and security.
Is AI enough to protect an organization from cyberattacks?
No. AI is one component of a broader cybersecurity strategy. Organizations still need security architecture, access controls, authentication, encryption, software updates, backups, vulnerability management, employee awareness, monitoring, and incident response.
Conclusion
AI is becoming an important part of modern cybersecurity because it can help process large volumes of security information, identify unusual activity, prioritize threats, and support security investigations.
At the same time, AI creates additional risks. Attackers can use AI to enhance existing cyber activities, while AI applications can themselves become targets through techniques such as prompt injection, data poisoning, adversarial attacks, and model manipulation.
Developments during 2025 and 2026 show growing international attention to AI security. NIST's Cyber AI Profile, ENISA's threat research, the EU AI Act, and the UK's AI Cyber Security Code of Practice all demonstrate increasing emphasis on securing AI systems and understanding AI-enabled cyber threats.
For organizations operating globally, effective AI cybersecurity requires a combination of technology, governance, risk management, data protection, security monitoring, testing, and human oversight. AI can strengthen cybersecurity capabilities, but its effectiveness depends on how securely it is designed, deployed, monitored, and maintained.
