What is AI in cybersecurity? Uses, benefits, risks, and examples
- ESET Expert
- 14 minutes ago
- 15 min read
AI gives security teams more speed and scale, but not a free pass on the basics.
Security teams use artificial intelligence (AI) to sift through huge volumes of telemetry, spot activity that deserves attention, and make investigations more efficient. At the same time, cybercriminals are experimenting with many of the same technologies to improve phishing campaigns, automate fraud, and increase the scale of their operations. That creates a lot of confusion.
Some headlines portray AI as the future of cybersecurity, while others present it as a new source of risk that defenders can’t control. Reality sits somewhere in the middle. AI is neither the enemy nor the answer to every security problem. In fact, according to the ESET SMB Cyber Readiness Index 2026, 73% of small and medium sized businesses (SMBs) are already integrating AI into their operations, yet 70% acknowledge that AI introduces new security risks. If used responsibly, it’s a tool that can help defenders work more effectively. If it’s used irresponsibly, it can create new risks.
Those organizations that are seeing the most value from AI aren’t trying to replace security professionals; they are instead using AI to help skilled practitioners make better decisions in less time.
Key points of this article:
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What is AI in cybersecurity?
AI in cybersecurity refers to the use of intelligent algorithms to help detect threats, analyze behavior, prioritize alerts, support investigations, and improve security operations. But that does not mean handing security decisions over to machines.
One of the most common misconceptions is that AI will replace cybersecurity teams. In practice, its real value comes from helping analysts focus on the activity that matters most. At its core, AI helps security teams deal with volume, which is a problem that keeps growing. A modern organization generates enormous amounts of security data, and every log-in attempt, endpoint activity, network connection, cloud event, and application interaction produces information that may or may not matter. Buried somewhere inside that data could be the first sign of an attack.
Where AI helps—and where humans still matter
Because human analysts can’t manually inspect every event, AI can help them narrow the field. Instead of having to review every event individually, defenders can use AI-driven systems to identify unusual activity, connect related events, and surface incidents that deserve immediate attention. The result is a more focused and efficient security operation.
However, human oversight remains essential because security decisions require context, judgment, and accountability that software can’t provide. People remain better at understanding business impact, weighing competing priorities, and deciding what action should ultimately be taken. That is why the strongest cybersecurity programs combine machine efficiency with human expertise.
The business demand for AI-driven security capabilities is also becoming more visible. When SMBs were asked how they would like to use AI within their cybersecurity strategies, the most common priorities were anticipating threats before they occur and faster identification and mitigation of attacks.
AI for cybersecurity vs. AI security
That distinction matters because discussions about AI and cybersecurity often blur two separate concepts. One focuses on using AI to improve security outcomes, while the other focuses on securing AI itself. Let’s examine the difference.
AI for cybersecurity
AI for cybersecurity refers to the use of AI technologies within security operations.
Examples include systems that:
Detect suspicious activity
Analyze malware
Correlate related events
Prioritize alerts
Support investigations
Recommend possible response actions
The goal is straightforward: to help defenders identify and respond to threats more efficiently.
AI security
AI security addresses a very different challenge. As organizations introduce AI assistants, ChatGPT-style tools, AI agents, autonomous workflows, and custom AI applications, they create new assets that need protection. Those systems may process sensitive information, access internal resources, or perform actions on behalf of users.
Thus, AI security focuses on protecting:
AI models
AI tools
AI agents
Prompts
Data sources
Integrations
Users interacting with AI systems
An organization may deploy AI-powered cybersecurity solutions to improve threat detection, while simultaneously adopting AI security solutions to protect its own AI ecosystem. Understanding the difference therefore helps organizations build stronger long-term security strategies.
How AI is used in cybersecurity
Most practical uses of AI in cybersecurity share a common objective: helping defenders find meaningful signals inside overwhelming quantities of data. The technology may look different from one platform to another, but the underlying challenge remains the same. Let’s explore several key fields where AI is used.
Threat detection
One of the oldest cybersecurity applications of AI is identifying suspicious files and malicious behaviors.
A notable example is ESET’s DNA Detections technology. Introduced in 2005, it extracts key characteristics from potentially malicious samples and creates broader detection profiles. Rather than relying only on exact matches, the technology helps identify previously unseen threats that share characteristics with known malware. The process is regularly refined through a combination of automated systems and researcher oversight.
This illustrates a key benefit of machine learning in cybersecurity. Defenders are often less interested in whether malware exactly matches a previously seen sample than whether it exhibits behavior associated with malicious activity.
What is machine learning?Machine learning helps systems identify patterns in data and improve their performance through experience. |
Malware analysis
AI also plays an important role when analysts encounter unknown files.
ESET LiveGuard Advanced provides a useful example of an AI-assisted cloud sandbox. Suspicious files are examined through multiple independent layers that include advanced scanning, AI-based analysis, sandbox execution, memory inspection, and behavioral evaluation. Rather than allowing one model to make a final decision, multiple analytical techniques contribute to the verdict, placing the submitted sample in one of four categories: malicious, highly suspicious, suspicious, or clean.
This approach reflects an important principle in cybersecurity: trust increases when results are validated through more than one method.
Understanding attacker behavior
The hardest security problems rarely involve a single alert. Defenders often need to understand how dozens, hundreds, or even thousands of events fit together.
This is where behavioral analytics becomes valuable since AI can identify various patterns. These may indicate credential abuse, lateral movement, privilege escalation, ransomware activity, or other forms of malicious behavior. Understanding attackers’ behavior is therefore a key capability making organizations more resilient.
Prioritizing investigations
It isn’t surprising that security teams frequently struggle with alert fatigue. The challenge is deciding what matters first, because it can impact your operability and business continuity.
For example, ESET LiveCortex was designed to address this problem. It consumes large volumes of customer-specific events, enriches them with context, identifies meaningful relationships, and helps separate important signals from background noise. Its purpose is to help defenders understand where they should focus their time and attention, while speeding up incident response where minutes can mean the difference between a damaging compromise and successful defense of a system.
Helping analysts work faster
Effective prioritization saves valuable analyst time and reduces the effort required to investigate routine alerts. Doing investigations at machine speed is one of the new layers of assistance for security teams that generative AI has introduced.
ESET LiveAI combines large language models with curated threat intelligence and retrieval-augmented generation techniques. It can help explain incidents, support investigations, generate reports, answer technical questions, and recommend possible remediation steps. To improve reliability, it operates within established guardrails and references supporting information rather than functioning as an unrestricted chatbot.
The most interesting aspect isn’t automation itself, but the real value of reducing the amount of time analysts spend translating technical findings into information that people can understand and act upon. These practical applications address some of the biggest challenges security teams face today. According to ESET’s research, keeping up with the latest cybersecurity threats (34%) and keeping up with emerging technologies such as AI (32%) are among the most frequently cited cybersecurity challenges for SMBs. AI can help reduce these pressures by accelerating analysis and helping defenders prioritize what matters most.
How attackers use AI in cyberattacks
Attackers and defenders alike are experimenting with AI, and both are pursuing many of the same major advantages: speed, scale, and efficiency. The fact is that in most cases, AI isn’t creating entirely new forms of cybercrime. Instead, it’s making familiar attacks easier to execute and harder to distinguish from legitimate activity.
A good example of this is how the perception of AI-driven threats doesn’t always align with reality. In the ESET SMB Cyber Readiness Index 2026, SMBs identified AI-powered malware as their leading concern for the year ahead. However, the most common causes of real-world incidents remain phishing campaigns, unpatched vulnerabilities, lack of security monitoring, and weak passwords. This highlights the importance of maintaining strong cybersecurity fundamentals while preparing for emerging AI-related risks.
Phishing
For years, defenders relied on poor grammar, unusual phrasing, and awkward wording as warning signs. Modern generative AI can produce convincing messages in seconds, reducing many of those obvious indicators. The same pattern appears elsewhere. AI is currently making phishing campaigns more scalable and more convincing rather than fundamentally changing attackers’ preferred methods of gaining initial access.
Attackers are also exploring ways to improve malware development. Social engineering campaigns can also be tailored more effectively to individual targets, and fraud operations can generate content at a volume that would have required extensive human effort only a few years ago.
Moreover, deepfake audio and video are being used to support impersonation and manipulation attempts. Besides, researchers are also examining how AI can be applied to vulnerability discovery, a capability that could benefit defenders and attackers alike.
Personalization
Another concern involves personalization. AI systems can process large amounts of publicly available information and help criminals identify vulnerable individuals, recruit participants into criminal ecosystems, or generate scams tailored to specific circumstances.
Small open-source models trained on stolen data may eventually make these activities even more effective. Still, defenders shouldn’t lose sight of the basics: identifying suspicious behavior, malicious infrastructure, fraudulent domains, harmful files, and other indicators of compromise often matters more than framing every challenge as AI versus AI.
Benefits of AI in cybersecurity
Now that we have the attackers’ advantages out of the way, let’s take a look at how AI benefits the defenders:
Speed: Much of the excitement surrounding AI comes from practical operational improvements rather than breakthrough discoveries. As discussed above, one advantage is speed. AI can help analysts work through large numbers of alerts more quickly, reducing the time required to determine which incidents deserve further investigation.
Scale: Scale is equally important. Security teams face a growing gap between the number of events generated by modern environments and the number of people available to review them. AI helps narrow that gap.
Context: Another benefit comes from context. Instead of presenting isolated alerts, AI systems can enrich findings with additional information and highlight relationships that might otherwise remain hidden.
Consistency: Consistency also matters since human analysts become fatigued and aren’t able to work seamlessly at such pace as automated systems. AI systems can evaluate large data sets consistently and at scale without the fatigue that affects human analysts.
Reduced repetitiveness: Perhaps most importantly, AI helps security professionals spend less time on repetitive work. When routine analysis requires fewer manual steps, defenders can focus more energy on threat hunting, incident response, risk management, and strategic projects.
Organizations increasingly view these efficiency gains as strategic advantages. SMBs identify faster attack mitigation and threat anticipation among the most valuable cybersecurity applications of AI. All these advantages are meaningful, but none of them provide guaranteed outcomes or eliminate the need for expertise. They simply allow that expertise to be applied more effectively.
Risks and limitations of AI in cybersecurity
Although focusing on the benefits of AI is important, understanding its limitations is just as valuable. The main point is simple: AI can help security teams work faster, but it still needs oversight, validation, and clear boundaries.
False positives, false negatives, and model drift
Let’s begin with one of the most familiar limitations: false positives and false negatives. No AI model will ever be 100% accurate, which means some mistakes will always exist to some degree.
False positives may incorrectly identify normal activity as malicious, creating unnecessary work for analysts. False negatives create the opposite problem, allowing dangerous activity to be overlooked entirely. The goal, therefore, is to design security systems that manage both through validation, human oversight, and additional protective layers.
Training data matters too. Models trained on incomplete, biased, or outdated information will produce weaker results, widening an organization’s exposure to threats. Performance can also decline over time through model drift, as cybersecurity environments, attacker behavior, and legitimate business activity change. An effective model today may become less effective tomorrow if it isn’t regularly updated and validated against current threats.
Manipulation, transparency, and data protection
Adversarial manipulation presents another challenge. Attackers actively study AI systems and look for opportunities to exploit weaknesses in their behavior, which can weaken detection accuracy and reduce overall defensive effectiveness.
Transparency also remains an ongoing concern. Organizations need to understand how important conclusions are reached, especially when recommendations influence investigations or remediation decisions. Poor data protection is another risk: sensitive information may be exposed if organizations fail to establish clear policies governing AI usage.
Cost and governance risks
Cost is another practical limitation. Autonomous AI agents can be expensive to run because each task may trigger repeated model calls, tool use, and workflow execution. Attackers may also attempt to exhaust these systems by giving them impossible or looping tasks, creating a denial-of-wallet scenario in which the owner faces unnecessary operational costs.
These concerns aren’t merely theoretical. ESET’s research shows that 70% of SMBs acknowledge that AI introduces additional security risks despite widespread adoption. Organizations, therefore, need governance, oversight, and visibility to benefit from AI without unnecessarily increasing exposure to cyber threats.
Data poisoning: a growing concern
Along the previous, data poisoning is also of great concern. It occurs when attackers intentionally manipulate information used to train AI models to influence how the model behaves.
If poisoned data enters a training process, the model may learn inaccurate relationships, become less reliable, or produce misleading outputs. In extreme cases, attackers could attempt to shape model behavior in ways that benefit their objectives.
This is one reason security vendors place significant emphasis on data quality, validation, and continuous monitoring. Trustworthy AI depends on trustworthy inputs. This principle is foundational to any effective AI security strategy.
However, the broader lesson extends beyond data poisoning. Organizations shouldn’t treat AI as an infallible black box, because strong cybersecurity programs rely on validation, transparency, and cross-checking. Those principles help reduce risk regardless of which AI technology is being used.
Why human expertise still matters
A useful way to think about AI is as a very smart but literal-minded assistant. Viewing AI as a replacement decision-maker is rarely helpful. As mentioned several times before, AI can process more data than any analyst. That’s simply how advanced computational capacity works. It can recognize patterns across enormous data sets, as well as surface incidents that deserve attention. What remains beyond the machine’s capabilities is understanding business priorities, operational constraints, risk tolerance, and organizational context.
As a security leader, one must balance competing priorities. Thorough investigation means one needs to consider intent, context, and impact. That’s a lot harder when we realize that incident responders regularly encounter situations that fall outside predefined rules. These activities depend on human judgment, and people still remain way better at evaluating and deciding what’s best for what organization.
The philosophy that the strongest cybersecurity strategies use AI to support experts appears throughout modern security operations. Therefore, it isn’t surprising that the future of cybersecurity isn’t human expertise versus AI, but human expertise amplified by AI.
Why AI security is important
As organizations adopt AI at scale, they face a new challenge: protecting AI systems from misuse, abuse, and unintended exposure, via:
Shadow AI
Many organizations are already dealing with what is commonly known as shadow AI. This is where employees use unauthorized tools outside established governance processes. This introduces deeper risks because AI tools may ingest sensitive data, learn from user inputs, and generate outputs that are difficult to predict or verify.
For companies, that can mean accidental exposure of confidential information, GDPR and privacy concerns, leakage of intellectual property, unreliable processes or code, and a gradual reduction in human accountability when employees rely too heavily on AI-generated results.
ESET research indicates that 40% of SMBs globally do not have a policy restricting the use of AI applications outside approved processes or platforms. As employees increasingly adopt AI tools in their daily work, the absence of clear governance increases the risk of shadow AI, data leakage, and compliance issues.
Data leakage
Prompt leakage is another concern. While the phrase is often used to describe attackers tricking an AI into revealing its internal system instructions, it’s also heavily used to describe AI data leakage where employees inadvertently paste sensitive corporate data into external, public consumer models.
Sensitive information entered into AI systems may be exposed to parties that should never have access to it. The most prominent real-world example occurred when engineers at Samsung’s Semiconductor Division used ChatGPT to optimize their workflows, which initially resulted in banning the service internally, and the company subsequently building its own chatbot to prevent such incidents.
Emerging complexity
The rise of AI agents introduces additional complexity. These systems increasingly rely on downloadable skills, external integrations, third-party repositories, and autonomous workflows. The shift from question-and-answer-based chatbots to autonomous AI agents transforms the risk landscape from a data leakage problem to an execution and control problem.
While chatbots merely process text, agents possess agency: the ability to plan, make decisions, use tools, and execute actions across enterprise networks. These agents simply expand the attack surface.
ESET's AI Skill Checker was developed to address this emerging risk. The technology analyzes skills before execution, evaluates their behavior, examines payloads and external resources, and repeats assessments whenever skills change. This repeated validation is important, because a skill that appears harmless today could behave very differently after an update.
Throughout ESET’s ongoing AI security research, the company has already analyzed roughly 1.1 million skills and identified thousands that warranted further investigation, including many considered suspicious or malicious.
Governance and oversight
These findings reinforce an important reality: organizations must secure AI environments in much the same way they secure endpoints, networks, cloud workloads, and identities. The fact is that AI has become an infrastructure layer with execution privileges, and that’s what makes governance, oversight, and visibility highly important.
Governance provides the rules, policies, and legal frameworks that dictate how AI can be built, purchased, and utilized across the organization. Oversight bridges the gap between raw technical data and human accountability. It ensures that as AI systems become more independent, they remain anchored to corporate safety boundaries. A lack of visibility equals lack of control, and with AI, this risk is amplified since AI traffic mimics normal web and API behaviors.
The importance of governance is becoming more apparent as AI adoption grows. The ESET SMB Cyber Readiness Index 2026 found that organizations with higher levels of AI adoption are generally more likely to implement AI policies intended to restrict unapproved AI usage and shadow AI practices.
AI observability
As a consequence of the above, AI observability and control-plane concepts are emerging in response to these needs. Organizations want to understand where AI is being used, which systems have access to sensitive data, and whether those interactions align with internal policies.
While AI observability focuses specifically on identifying and monitoring AI usage, organizations should also explore agent-based capabilities that can assess the broader network environment and uncover even obscure or hard-to-detect assets.
To address these growing challenges, the ESET PROTECT Platform enables AI observability and will be further extended with AI-powered asset discovery.
How to evaluate AI cybersecurity solutions
The AI market is crowded with ambitious claims. While some are credible, others are difficult to verify. Organizations evaluating AI-enabled security technologies should begin with a simple question: where exactly is AI being used? The answer should be as specific as possible.
An effective evaluation framework should include the following:
Ask vendors to explain what their AI component does, how outputs are validated, and how the technology contributes to measurable outcomes.
Find out more about how hallucinations are handled, whether results can be traced to supporting evidence, and how much human oversight remains part of the workflow.
Make sure to understand what information is collected, where it is processed, and how privacy is maintained.
Consider operational fit, because even an impressive AI capability provides limited value if it disrupts existing Security Operations Center (SOC) workflows or creates additional complexity.
Verify how the solution controls autonomous workflows, handles third-party plug-in skills, and restricts API write-access to prevent unauthorized network actions.
Verify how vendors defend their tools against direct or indirect prompt injections, adversarial data poisoning, and system-prompt exfiltration attempts.
Verify what the underlying model is, whether it is fully custom and owned by the vendor, or whether it depends on third-party or open-weight models that may introduce unwanted legal, operational, or security dependencies; also understand how often the model is updated, how open-source dependencies and orchestration frameworks are vetted, and whether changes introduce new logic or compliance risks.
Ensure the solution maintains a permanent, tamper-proof log of all prompt-and-response interactions, allowing security teams to reconstruct events during a forensic breach investigation.
Generally speaking, transparency should outweigh hype and evidence should outweigh promises. And any vendor presenting AI as a fully autonomous replacement for security professionals deserves careful scrutiny.
Conclusion
AI is rapidly changing cybersecurity, but not in the way many headlines suggest. While it helps defenders detect threats, prioritize investigations, and respond faster, attackers are using the same technology to scale phishing, social engineering, and fraud.
The most effective security strategies focus on both realities: embracing AI’s strengths while addressing its risks through governance, visibility, and human oversight. Ultimately, AI isn’t a replacement for cybersecurity professionals. It’s a force multiplier that helps skilled teams make faster, better-informed decisions while maintaining the strong security fundamentals that continue to stop the majority of attacks.
FAQ: AI in Cybersecurity
What is AI in cybersecurity?
AI in cybersecurity refers to using machine learning and related technologies to help security teams detect threats, analyze behavior, prioritize alerts, and make investigations more efficient.
What are examples of AI in cybersecurity?
Common examples include AI-assisted threat detection, malware analysis, alert prioritization, behavioral analytics, and investigation support. In ESET’s portfolio, examples include ESET DNA Detections, ESET LiveGuard Advanced, ESET LiveCortex, and ESET LiveAI.
How do attackers use AI?
Attackers use AI to make phishing, social engineering, fraud, and scam campaigns more convincing and scalable. In many cases, AI does not create entirely new attacks; it makes familiar ones faster and harder to spot.
What are the benefits of AI in cybersecurity?
AI can help security teams process large volumes of data, reduce repetitive work, prioritize investigations, add context to alerts, and give analysts more time to focus on higher-value security decisions.
What are the risks of AI in cybersecurity?
Risks include inaccurate outputs, poor training data, model drift, adversarial manipulation, limited transparency, data poisoning, sensitive-data exposure, and overreliance on automation without human oversight.
Can AI replace cybersecurity professionals?
No. AI can help defenders work faster and more efficiently, but cybersecurity still depends on human judgment, business context, accountability, and decision-making.
What is the difference between AI cybersecurity and AI security?
AI cybersecurity usually means using AI to improve threat detection and security operations. AI security means protecting AI systems themselves, including models, prompts, agents, integrations, data, and user interactions.
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