Artificial intelligence is changing cybersecurity in three connected ways. Attackers can use AI to create more convincing scams and support other malicious activity. Security teams can use AI-enabled tools to analyze information and investigate threats. At the same time, the AI systems organizations adopt can introduce security risks of their own.
For cybersecurity professionals, that creates a need to understand both the technology and the decisions around it. An AI tool can help an analyst make sense of an alert, but someone still needs to check the evidence, understand the business impact, and decide what should happen next. Professionals who can connect technical knowledge with risk management and clear communication will be better prepared to contribute as organizations adopt these tools.
How Is AI Used in Cybersecurity?
AI is a broad term for technologies that perform tasks associated with human intelligence. Machine learning is one approach that uses data to identify patterns or make predictions. Generative AI produces content such as text, images, or code in response to an instruction. Security products may use one or more of these approaches, and their capabilities depend on the data, design, and controls behind them.
In a security operations setting, an AI-enabled tool might flag unusual account activity, group related alerts, or help summarize information for an analyst. Consider a user account that signs in from an unexpected location and then accesses a large number of files. The tool may identify the pattern as unusual; the analyst must determine whether it reflects legitimate work, a configuration issue, or a possible compromise.
These tools can assist with prioritization, but their output is not proof that an incident has occurred. The National Institute of Standards and Technology (NIST) notes that AI-assisted threat hunting can create false positives as well as improve detection. A security team needs processes for checking alerts, measuring performance, and correcting errors.
How Attackers Can Use AI
AI can make some established attacks easier to produce or adapt. A criminal may use generative tools to draft messages that imitate a familiar writing style, translate a scam into another language, or tailor a request to information found online. A message that appears polished and personal can still be fraudulent. Organizations should train employees to verify unusual requests through a trusted channel, especially when the request involves credentials, payments, or sensitive information.
Synthetic audio or video can add another layer to impersonation. Imagine a finance employee receiving a voice message that sounds like a senior leader and requests an urgent payment. A verification process that requires a callback using a known number or a separate approval can reduce reliance on how convincing the message sounds. The point is to check the request, not to assume a person can always identify manipulated media by listening closely.
AI may also help attackers with parts of their technical workflow, including analyzing information or adapting malicious content. These capabilities do not mean every attack is fully automated or that traditional attacks have disappeared. Security teams still need to address vulnerabilities, protect accounts, monitor systems, and prepare to respond when something goes wrong.
AI Systems Can Be Targets, Too
Organizations using AI need to protect the systems and information that support it. One concern is prompt injection: an attacker places instructions in material an AI system reads, attempting to make the system treat those instructions as commands. For example, an AI assistant summarizing a document might encounter hidden instructions that attempt to redirect its behavior. The consequences depend on what the system can access and what actions it is allowed to take.
NIST’s work on AI agent hijacking describes this type of indirect attack and its potential to cause unintended actions. Security teams can reduce exposure by limiting an AI system’s permissions, separating untrusted content from trusted instructions, testing realistic scenarios, and requiring approval for sensitive actions. These controls reduce risk, but no single safeguard eliminates every possible attack.
Other concerns include exposure of sensitive data, misuse of system access, and manipulation of data used by a model. NIST’s adversarial machine learning taxonomy describes categories such as data poisoning and privacy attacks. Professionals evaluating an AI system should consider the entire workflow: where information comes from, what the tool can access, who can use it, and how its behavior is monitored.
How AI Can Support Cybersecurity Defenses
Security teams work with information from many sources, including network activity, account activity, and reports from users. AI-enabled tools can help identify patterns, group related signals, and bring potentially important events to an analyst’s attention. A generative assistant may also help a team summarize a complicated incident record or prepare a first draft of a report.
The benefit depends on the quality of the information and the way the tool is used. A summary may omit an important detail or state an inference as fact. An alert may reflect normal activity that the tool has not seen before. Analysts should compare outputs with the underlying logs and other evidence, document their reasoning, and escalate incidents according to established procedures.
Some organizations may use automation to carry out a response, such as temporarily restricting a suspicious account. That decision requires safeguards: clearly defined triggers, appropriate permissions, an ability to reverse an action, and a plan for responding when the automation is wrong. Blocking a legitimate user can interrupt business operations, so response speed and potential disruption both matter.
What Skills Do Cybersecurity Employers Look For?
The specific skills employers request vary by role. An analyst investigating alerts, a security manager evaluating a vendor, and a researcher studying AI system vulnerabilities will not need identical experience. Across those settings, several capabilities help professionals use AI responsibly and explain the resulting decisions.
Security fundamentals. Understanding networks, identity and access, system vulnerabilities, and incident response gives professionals the context to interpret an AI-generated alert. Without that foundation, it is difficult to tell whether an output is relevant or what evidence would confirm it. The Bureau of Labor Statistics profile of information security analysts describes work that includes monitoring networks, investigating breaches, and evaluating vulnerabilities.
Data and analytical judgment. Professionals need to ask where information came from, whether it is complete, and how a tool arrived at a result when that information is available. They should be able to investigate both false alarms and missed threats. A useful question is not simply “What did the tool say?” but “What evidence supports that conclusion, and what information is missing?”
AI literacy. Security professionals do not all need to build machine learning models, but they benefit from understanding what their organization’s tools do, their limitations, and the risks associated with their use. That includes recognizing when a generative system may produce an unsupported answer and when an AI-enabled workflow has access to sensitive information or tools.
Risk management and governance. Someone must decide what information a vendor may process, who can approve automated actions, and how a system’s performance will be reviewed. These questions connect technical controls with organizational priorities. Our guide to what cybersecurity risk management involves explains how teams assess exposure and choose responses.
Communication and collaboration. Security decisions often involve IT, operations, legal, procurement, and business leaders. An effective professional can explain the significance of an AI-related risk without assuming everyone understands the underlying technology. For example, a recommendation to restrict an AI assistant’s access should explain the information at risk, the proposed control, and how the change could affect employees’ work.
What Cybersecurity Careers Involve AI?
An information security analyst may encounter AI through alert investigation, monitoring, or the assessment of new systems. A security engineer or architect may help design protections for an AI-enabled application. A governance or risk professional may evaluate a vendor, document acceptable uses, or help establish oversight. The exact responsibilities depend on the employer and the maturity of its security program.
At the leadership level, a chief information security officer may help determine how AI fits into the organization’s security strategy and how its risks are communicated to executives. That role generally requires substantial experience in addition to education. Read more about what a CISO does and the education that can support the role.
Research is another possible direction. A professional might investigate how an organization assesses AI-enabled threats or how a particular control performs under defined conditions. The research question and available evidence determine the appropriate method. A doctorate can support that kind of advanced inquiry, although it is not required for every cybersecurity role.
Will AI Replace Cybersecurity Professionals?
AI will change some tasks, but organizations still need people to investigate uncertain situations, make decisions, and accept responsibility for security outcomes. An automated system may identify unusual activity; a professional must consider whether that activity is harmful, what additional evidence is needed, and how a response might affect operations. Human oversight also matters when a tool’s output is incomplete or wrong.
The field’s future is not limited to a single job title. The Bureau of Labor Statistics projects 21% employment growth for information security analysts from 2025 to 2035. That is a projection for the occupation as a whole, not a prediction for AI-specific jobs or a guarantee of employment for graduates.
How Can Education Help You Prepare?
For professionals who want broader preparation for managing organizational security risks, the University of Fairfax Master of Science in Cybersecurity Management is an option to explore. The program’s security and management focus can provide context for evaluating emerging technologies, coordinating security decisions, and communicating risk. Review the current curriculum with admissions to determine how it fits your background and goals.
For experienced professionals interested in original research on information assurance problems, the Doctorate of Information Assurance offers a doctoral pathway to consider. A possible research interest might concern the governance or evaluation of AI-enabled security practices, subject to program guidance and research approval. The program includes original dissertation research; prospective students should discuss topic fit and faculty support before enrolling.
Neither program should be assumed to offer a dedicated AI specialization solely because AI is relevant to cybersecurity. Ask about current course content, research opportunities, admission requirements, and the technical experience that would strengthen your plan. Our comparison of cybersecurity certificates, master’s degrees, and doctorates can help you choose the level of study that matches your next step.
Build Your AI and Cybersecurity Skills
Start by identifying the security responsibilities you want to take on, then compare them with your current experience. Practice examining alerts, documenting evidence, and explaining a recommended response. If you use an AI assistant, work within your organization’s policies and avoid entering sensitive information into a tool that has not been approved for that purpose.
As your interests develop, explore the University of Fairfax master’s in cybersecurity management or Doctorate of Information Assurance. The best preparation connects emerging AI knowledge with the security judgment, communication, and research skills needed for the work you want to do.