AI in Cybersecurity (2026): Uses, Risks and Real Costs

Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Breach-cost data, AI security standards and courses re-checked at the source on 21 September 2026. See our review methodology.

By Josh Hutcheson · E-Learning Specialist

Reviewing online learning platforms since 2019. Review methodology

THE SHORT ANSWER

Bottom line: AI now sits on both sides of cybersecurity. Defenders use it to detect threats faster and cut the cost of breaches; attackers use it for deepfakes, phishing and adaptable malware; and the AI systems companies deploy have become a new target in their own right. The advantage goes to teams that pair AI tools with strong fundamentals and human judgment.

Breach costs: a record USD 4.99 million on average, up 12% (IBM, 2026).
AI attacks: up 56%, led by deepfake impersonation and AI-enabled malware (IBM, 2026).
AI defense pays: extensive security AI and automation saves USD 1.93 million per breach (IBM, 2026).
Top risk for AI apps: prompt injection, number one in the OWASP Top 10 for LLM applications (2025).

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What AI in cybersecurity means

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The phrase covers three different things, and it helps to keep them apart:

AI for defense: machine learning and generative AI that help security teams detect, investigate and respond to threats.
AI for attack: criminals and state actors using the same technology to make attacks cheaper, faster and more convincing.
Security of AI: protecting the models, data and AI agents organizations now run, which introduce new kinds of vulnerabilities.

Traditional security tools rely heavily on known signatures and fixed rules. AI adds the ability to learn what normal behavior looks like and flag deviations, to process far more data than analysts can read, and, with generative AI, to summarize and explain what is happening in plain language.

The numbers in 2026

IBM and the Ponemon Institute’s Cost of a Data Breach Report 2026 puts the global average cost of a breach at USD 4.99 million, a 12% increase on the previous year and a record high, driven by higher detection, escalation and lost-business costs. The same research found AI-driven attacks increased 56%, led by AI deepfake impersonations and AI-enabled malware, and that an AI model inversion attack, where attackers extract sensitive training data from a model, cost an average of USD 6 million. On the defensive side, organizations using security AI and automation extensively saved USD 1.93 million per breach compared with those using none (IBM).

Taken together, the figures describe an arms race: AI makes attacks more damaging and more frequent, and it also makes a large, measurable difference to how well defenders cope.

How defenders use AI

1. Threat detection and anomaly spotting

Machine learning models learn the normal patterns of a network, device or cloud account and flag unusual activity, such as data moving at odd hours, a server contacting an unfamiliar country or a process behaving like ransomware. This catches new attacks that have no known signature, which rule-based tools miss.

2. Behavioural analytics for users and entities

User and entity behavior analytics (UEBA) builds a baseline for each person and system: where they log in from, which files they touch, how much data they move. A sudden change, such as an employee account downloading thousands of records or signing in from two continents an hour apart, raises a risk score. This is one of the main defenses against stolen credentials and insider threats.

3. Phishing and fraud detection

Email security and fraud systems analyze language, sender behavior, links and transaction patterns to block phishing and flag fraudulent payments. As attackers use generative AI to write fluent, personalized messages, detection has moved from spotting bad grammar to analyzing context and behavior.

4. Security operations: alert triage and investigation

Security operations centers receive far more alerts than analysts can review. AI groups related alerts into incidents, ranks them by likely severity and, with generative AI assistants, summarizes what happened and suggests next steps in plain language. The effect is to let analysts spend time on the incidents that matter rather than on noise.

5. Vulnerability management and secure code

AI tools scan code and configurations for weaknesses, help prioritize which of thousands of known vulnerabilities to patch first based on real-world exploitation, and suggest fixes. The same tools help developers catch insecure patterns before code ships, including in code that other AI assistants wrote.

6. Identity and authentication

Biometric sign-in such as face and fingerprint recognition relies on machine learning, and risk-based authentication uses AI to decide when to ask for extra verification, for example when a login comes from a new device or location. With identity now the main target of many attacks, this is a core use.

7. Automated response

When confidence is high, security platforms can act automatically: isolating an infected laptop, disabling a compromised account or blocking a malicious domain in seconds. Most teams keep a person in the loop for high-impact actions, because an automated mistake can disrupt the business.

How attackers use AI

Deepfake impersonation. Cloned voices and synthetic video of executives are used to authorize fraudulent payments or trick staff into sharing access; IBM names deepfake impersonation as a leading driver of the 56% rise in AI-driven attacks.
Better phishing at scale. Generative AI writes convincing, error-free messages tailored to the target, in any language.
Adaptive malware. AI-enabled malware can change its code or behavior to evade detection, the modern form of the “mutating malware” that security teams have worried about for years.
Faster reconnaissance. AI tools sift public information and exposed systems to find weak points quickly.
Lower barriers to entry. Less-skilled criminals can get help writing scripts and exploits, although mainstream AI services try to block this.

The practical response is layered: verify payment and access requests through a second channel, train staff to expect convincing fakes, and assume that some phishing will get through.

Securing AI systems themselves

Every chatbot, AI agent and machine learning model an organization deploys is also an attack surface. The OWASP Gen AI Security Project’s 2025 Top 10 for large language model applications ranks prompt injection first, where instructions hidden in input or in content the model reads override its intended behavior, followed by sensitive information disclosure (OWASP). Other risks include poisoned training data, model theft and inversion attacks that extract private data, and AI agents given more permissions than they need.

Good practice is to treat AI components like any other untrusted software: limit what data and systems they can reach, validate their inputs and outputs, log their actions, and keep people accountable for consequential decisions. Regulation is adding requirements too. In the EU, AI Act transparency rules have applied since 2 August 2026, requiring people to be told when they are interacting with an AI system and certain AI-generated content to be labeled (European Commission).

Use cases compared

Use case	How AI helps	Main limitation
Threat detection	Finds unknown attacks by spotting anomalies	False positives if the baseline is poor
Behavioural analytics	Catches stolen credentials and insider threats	Needs time and clean data to learn normal behavior
Phishing and fraud	Analyses context, not just keywords	AI-written lures keep improving
SOC triage	Groups and prioritizes alerts, summarizes incidents	Summaries can be wrong; analysts must check
Vulnerability management	Prioritizes patches by real-world risk	Depends on accurate asset inventories
Authentication	Adds verification only when risk is high	Biometric data needs strong protection
Automated response	Contains threats in seconds	Mistakes can disrupt the business

The limits and challenges

Cost and complexity. AI security platforms, the data infrastructure they need and the people to run them are expensive, which puts advanced tooling out of reach for many small organizations.
False positives and alert fatigue. A model that flags too much is ignored; one that flags too little misses attacks. Tuning takes ongoing work.
Data quality. Detection is only as good as the logs and telemetry it sees.
Adversarial evasion. Attackers study defensive models and craft activity to slip under them.
Over-reliance. Generative AI assistants can produce confident but wrong analysis, so human review stays essential.
Skills gaps. Teams need people who understand both security and how AI systems work.

How organizations should start

Get the fundamentals right first: asset inventory, patching, multi-factor authentication and logging. AI cannot compensate for missing basics.
Pick one high-volume problem, such as alert triage or phishing, and measure time saved and incidents caught.
Inventory your own AI use, including staff using public AI tools, and set rules for what data may be shared with them.
Secure AI deployments against the OWASP Top 10 for LLM applications, with least-privilege access for AI agents.
Train people to recognize deepfakes and AI-written phishing, and to verify unusual requests through a second channel.

A checklist for individuals and small businesses

Most of the tools above are built for large security teams, but the AI-driven threats reach everyone. A few habits cover most of the risk:

Agree a verification rule for money and access: any request to pay, change bank details or share a password is confirmed by calling a known number, never by replying to the message, however real the voice or video seems.
Turn on phishing-resistant sign-in such as passkeys or an authenticator app for email, banking and admin accounts.
Keep devices and software updated automatically, since many attacks still exploit known flaws.
Be careful what you paste into public AI tools; treat them like a public website unless your plan guarantees your data is not used for training.
Back up important data offline or to a separate account so ransomware cannot hold it hostage.

Careers in AI and cybersecurity

Demand for security skills remains strong. The US Bureau of Labor Statistics reports a 2025 median wage of $129,180 for information security analysts and projects employment growth of 21% from 2025 to 2035, much faster than average (BLS). Roles that combine security with AI, such as detection engineering, AI red-teaming and security for machine learning systems, are among the fastest-developing specialisms. Most people enter through general security roles and add AI skills on top. Our guides to the best cybersecurity courses and CompTIA Security+ training cover the foundations.

Courses to build AI security skills

Johns Hopkins University – AI for Cybersecurity (Coursera). A three-course specialization taught by Lanier Watkins: Introduction to AI for Cybersecurity, Advanced Malware and Network Anomaly Detection, and Securing AI and Advanced Topics, which covers generative adversarial networks, adversarial attacks on AI systems and reinforcement learning for adaptive defenses. The most technical option here, best if you already have security or programming basics.
IBM – Generative AI for Cybersecurity Professionals (Coursera). A three-course specialization taught by Antonio Cangiano: an introduction to generative AI tools and models, prompt engineering basics, and a course on applying generative AI to standard and advanced cybersecurity tasks such as incident management. Less technical than the Johns Hopkins program and suited to working security staff. More than 12,000 learners enrolled.
Google – Cybersecurity Professional Certificate (Coursera). The foundation option for beginners: security fundamentals, networking, Linux, SQL, Python and incident response, before specializing in AI.

All three are included in Coursera Plus. Coursera removed its free audit option for most courses in 2025, so check the price before enrolling.

See Johns Hopkins AI for Cybersecurity →

For the machine learning side, see our guide to machine learning courses, and for the wider context our pages on network security threats and AI trends.

See IBM Generative AI for Cybersecurity →

AI security terms explained

Term	What it means
Prompt injection	Hidden or malicious instructions in input or in content an AI reads that override its intended rules
Jailbreak	A prompt crafted to get a model to ignore its safety restrictions
Data poisoning	Corrupting training data so a model learns wrong or hidden behavior
Model inversion	Querying a model to reconstruct sensitive data it was trained on
Adversarial example	Input subtly altered to make a model misclassify it, such as malware tweaked to look benign
Deepfake	Synthetic audio, image or video that imitates a real person
UEBA	User and entity behavior analytics: baselining normal behavior to spot anomalies
SOAR	Security orchestration, automation and response: platforms that automate investigation and response steps
AI red-teaming	Deliberately attacking an AI system to find weaknesses before real attackers do

Knowing these terms is increasingly expected in security interviews, and they map directly onto the risks in the OWASP Top 10 for LLM applications.

Frequently asked questions

How is AI used in cybersecurity?

Defenders use AI to detect unusual activity on networks and endpoints, spot phishing and fraud, prioritize alerts in security operations centers, find vulnerabilities in code, and power risk-based authentication. Attackers use it too, for convincing phishing, deepfake impersonation and adaptable malware, and AI systems themselves now need securing.

Does AI actually reduce the cost of data breaches?

According to IBM’s Cost of a Data Breach Report 2026, organizations that use security AI and automation extensively save an average of USD 1.93 million per breach compared with those that use none. The global average cost of a breach reached a record USD 4.99 million in the same report.

Will AI replace cybersecurity jobs?

Unlikely. AI takes over repetitive work such as sorting alerts and summarizing incidents, but attackers are using AI as well, which increases the need for skilled people to investigate, make decisions and secure AI systems. The US Bureau of Labor Statistics projects 21% growth in information security analyst jobs from 2025 to 2035.

What is prompt injection?

Prompt injection is an attack where instructions hidden in user input or in content an AI system reads, such as a web page or document, make the model ignore its intended rules, leak data or take unwanted actions. OWASP ranks it as the number one risk in its 2025 Top 10 for large language model applications.

How are attackers using AI?

IBM’s 2026 report found AI-driven attacks rose 56%, led by deepfake impersonation and AI-enabled malware. Attackers also use generative AI to write more convincing phishing messages in many languages, automate reconnaissance and help less-skilled criminals write malicious code.

What skills do you need to work in AI security?

A foundation in core security (networking, operating systems, identity, incident response), working knowledge of machine learning and how models are built and deployed, scripting in Python, and familiarity with AI-specific risks such as prompt injection, data poisoning and model theft.

The verdict

AI has become essential to cybersecurity because attacks now move faster and at greater scale than people alone can handle, and the evidence that it helps is concrete: IBM finds extensive use of security AI and automation saves nearly USD 2 million per breach. It is not a replacement for fundamentals or for skilled people, and it brings new risks, from deepfake fraud to prompt injection. The organizations and professionals who do best treat AI as a powerful tool that still needs direction, verification and security of its own.

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