AI in Cybersecurity: How It’s Used and Key Developments in 2026

Artificial intelligence (AI) and machine learning have powered cybersecurity tools for decades, from antivirus and spam filtering to phishing detection. The biggest change in 2026 is autonomy. AI systems are evolving from tools that summarize findings and recommend next steps into agents that can investigate incidents, use external tools, and take action with varying degrees of human oversight.

This shift is transforming cybersecurity on both sides. Defenders are using AI to analyze threats, test controls, and respond at machine speed. Attackers are using it to accelerate vulnerability research, social engineering, malware development, and other stages of the attack lifecycle. At the same time, rapid AI adoption is creating new risks involving prompt injection, excessive permissions, sensitive data exposure, and insecure integrations.

To understand how AI is shaping cybersecurity in 2026 and beyond, we’ll explain how AI is used in cybersecurity today, the benefits it provides, the latest developments, and how attackers are also exploiting it.

How is AI used in cybersecurity?

AI is used in cybersecurity to automate tasks that are highly repetitive, manually-intensive, and tedious for security analysts and other experts to complete. This frees up time and resources so cybersecurity teams can focus on more complex security tasks like policymaking.

Take endpoint security, for example. Endpoint security refers to the measures an organization puts in place to protect devices like desktops, laptops, and mobile devices from malware, phishing attacks, and other threats. To supplement the efforts of human experts and the policies they put in place to govern endpoint security, AI can learn the context, environment, and behaviors associated with specific endpoints as well as asset types and network services.

It can then limit access to authorized devices based on these insights and prevent access entirely for unauthorized and unmanaged devices.

AI is also increasingly being connected to security tools, internal data, APIs, and business systems. These connections enable AI agents to do more than analyze information. They can investigate alerts, test hypotheses, create tickets, update configurations, and initiate response workflows.

This makes authorization and oversight essential. The more access an AI system has, the greater the potential impact if it makes a mistake, follows malicious instructions, or is compromised.

Benefits of AI in Cybersecurity

Cybersecurity presents unique challenges, including a constantly evolving threat landscape, vast attack surface, and significant talent shortage.

Since AI can analyze massive volumes of data, identify patterns that humans might miss, and adapt and improve its capabilities over time, it has significant benefits when applied to cybersecurity, including:

• Improving the efficiency of cybersecurity analysts
• Identifying and preventing cyber threats more quickly
• Effectively responding to cyber attacks
• Reducing cybersecurity costs
• Connecting security signals to provide greater context
• Continuously testing systems, controls, and configurations
• Accelerating vulnerability remediation

According to IBM’s 2026 Cost of a Data Breach Report, organizations with extensive use of AI and automation across their security operations reduced breach costs by an average of $1.93 million and shortened breach lifecycles by 65 days compared to organizations that did not use these technologies. Only about one-third of surveyed organizations reported extensive use across the security lifecycle.

IBM also found that one in four malicious breaches was AI-enabled. These incidents cost an average of $6 million, approximately $1 million more than the global average of $4.99 million.

These findings underscore an important point: Organizations must secure the AI systems they deploy while also using AI to strengthen their defenses.

To better understand the impact of AI on cybersecurity, let’s take a look at some specific examples of how AI is used in cybersecurity below.

AI in cybersecurity examples

Many organizations are already using AI to help make cybersecurity more manageable, more efficient, and more effective. Below are some of the top applications of AI in cybersecurity.

1. Threat detection

Threat detection is one of the most common applications of AI in cybersecurity. AI can collect, integrate, and analyze data from hundreds and even thousands of control points, including system logs, network flows, endpoint data, cloud API calls, and user behaviors. In addition to providing greater visibility into network communications, traffic, and endpoint devices, AI can also recognize patterns and anomalous behavior to identify threats more accurately at scale.

For example, legacy security systems analyzed and detected malware based on signatures only, whereas AI- and ML-based systems can analyze software based on inherent characteristics, like whether it is designed to rapidly encrypt many files at once, and tag it as malware. By identifying anomalous system and user behavior in real time, these systems can block both known and unknown malware from executing, making them more effective than signature-based technology alone.

2. Threat management

Another top application of AI in cybersecurity is threat management.

Security teams often receive alerts from dozens of tools but lack the context required to determine which issues present the greatest risk. They may struggle to answer fundamental questions such as where sensitive data resides, who has access to it, and whether an unusual event represents malicious activity or expected behavior.

AI can correlate information across security systems, asset inventories, identity platforms, and threat intelligence sources. This helps teams filter out low-priority activity, connect related events, and surface the findings most likely to require immediate attention. By reducing the time required to analyze, investigate, and prioritize alerts, security teams can spend more time remediating them.

3. Threat response

AI is also used effectively to automate certain actions to speed up incident response times. For example, say a known sample of malware shows up on an end user’s device. An automated response may immediately shut down that device’s network connectivity to prevent the infection from spreading to the rest of the company.

AI-driven automation capabilities can not only isolate threats by device, user, or location, but also initiate notification and escalation measures. This enables security experts to spend their time investigating and remediating the incident.

More advanced systems can gather evidence, reconstruct an incident timeline, and coordinate response actions across multiple tools.

4. Insider-risk detection and adaptive access control

Hybrid and remote work have expanded insider risk. AI tools can identify unusual user behavior that may indicate credential misuse or an insider threat before it escalates.

For example, behavioral analytics can flag an employee who suddenly downloads a large amount of sensitive information outside normal working hours. Security teams can then investigate the activity and determine whether it reflects a legitimate business need, a compromised account, or malicious behavior.

AI is also making zero trust more adaptive. Instead of simply verifying a password or issuing the same multifactor authentication prompt every time, systems can consider device health, location, user behavior, resource sensitivity, and network activity before granting access.

Behavioral anomalies do not necessarily indicate malicious intent. Organizations should implement these systems with appropriate privacy protections, documented monitoring policies, and human review.

5. Security and compliance automation

Security questionnaires are a common way to vet potential vendors and other third parties to assess whether their cybersecurity practices meet internal and external requirements. While these questionnaires are important for vendor risk management, they can take up valuable time.

AI can help speed up this process by suggesting answers based on previously answered questionnaires. More advanced tools can also pull from an organization’s security policies, controls, tests, audit reports, and other approved sources when suggesting answers.

AI can support the other side of the vendor-review process by extracting relevant information from documents such as SOC 2 reports. It can also help draft policies, evaluate risks, validate audit evidence, and generate remediation guidance.

These capabilities can reduce repetitive work, but organizations should require subject-matter review of AI-generated answers, document summaries, and risk recommendations. Incorrect or unsupported output can create contractual, compliance, and security risk.

Latest developments in cybersecurity AI in 2026

AI is moving from systems that primarily generate content to agents capable of taking action. This evolution is creating major opportunities for defenders, but it also changes the risks organizations need to manage.

1. Security copilots are becoming autonomous agents

Earlier generations of security AI primarily summarized alerts, answered questions, or suggested remediation steps. Agentic systems can now perform multistep work, including gathering context, querying tools, testing hypotheses, opening tickets, changing configurations, and initiating response actions.

This can reduce investigation and response times. It also changes the security question from what the model can say to what the system can access and do.

As AI assumes more investigative and operational work, the role of the security professional will evolve as well.

“The security professional’s role shifts from active responder to orchestrator: setting policy, validating the integrity of defensive systems, and eliminating blind spots rather than triaging an endless queue of low-level alerts,” Shrav Mehta, Founder and CEO of Secureframe, explained in a July 2026 Unite.AI interview.

Organizations deploying security agents should limit their access to the data and tools required for their intended purpose. High-impact actions should require human approval, and organizations should maintain a clear record of what each agent does.

2. Security testing is becoming continuous

AI systems are increasingly capable of pursuing a security objective over time instead of completing a single, isolated task. This could transform vulnerability management and penetration testing.

Instead of evaluating an environment once or twice a year, AI agents can continuously examine changing code and infrastructure, investigate potential vulnerabilities, and simulate attack paths. Continuous testing provides a more current picture of risk and can examine more code paths and attack scenarios than a human team could reasonably assess on its own.

Point-in-time validation is increasingly inadequate when software is updated and deployed continuously. A penetration test or control assessment provides useful evidence about a particular moment, but that evidence may become outdated as configurations drift, employees change roles, new vendors are introduced, and applications gain new integrations.

Human oversight remains essential, particularly when interpreting ambiguous findings, authorizing intrusive tests, or deciding how to address risks that affect critical systems.

3. AI is shrinking the vulnerability-response window

AI-assisted vulnerability research and exploit development are accelerating the race between attackers and defenders. The 2026 Verizon Data Breach Investigations Report found that vulnerability exploitation accounted for 31% of breaches, surpassing stolen credentials as the leading initial access vector for the first time in the report’s history. Verizon also warned that AI is compressing some exploitation timelines from months to hours.

This makes continuous asset discovery, risk-based patch prioritization, and automated remediation increasingly important. Security teams should assume that a publicly disclosed vulnerability may be exploited faster than traditional patching schedules allow.

AI can also help defenders respond more quickly by identifying affected assets, evaluating their exposure, and generating remediation guidance. Secureframe Comply AI for Remediation, for example, provides infrastructure-as-code guidance tailored to failing cloud tests across AWS, Microsoft Azure, and Google Cloud Platform. AI-generated fixes should be reviewed and tested before deployment, particularly when they affect production systems or sensitive data.

4. Natural-language interfaces are changing security operations

Generative AI allows analysts to query security data in natural language instead of relying exclusively on complex query languages. AI systems can summarize alerts, explain malicious code, construct searches, and present investigation results in a more accessible form.

Natural-language interfaces are also expanding beyond individual security products. Model Context Protocol, or MCP, provides a standardized way for AI assistants to connect to external tools and data sources. This can help security and compliance teams find information and complete work across multiple systems without repeatedly switching interfaces.

Because connected assistants can interact with real systems, organizations should evaluate each integration’s permissions and available actions before deployment.

5. AI applications have become an attack surface of their own

As organizations connect models to internal knowledge bases, APIs, plug-ins, and business systems, attackers gain new paths to sensitive information and privileged actions.

Common AI application risks include:

• Direct and indirect prompt injection
• Excessive agency and overly broad permissions
• Insecure tool, plug-in, and API integrations
• Sensitive information disclosure
• Data or model poisoning
• Improper output handling
• Cloud misconfigurations affecting AI workloads
• Supply-chain weaknesses in models, components, and training data

IBM reported that more than 20% of organizations in its 2026 breach study experienced a breach targeting an AI model or application. The most common causes included compromised APIs, applications, or plug-ins and cloud misconfigurations affecting AI workloads.

The OWASP Top 10 for LLM Applications 2026 reflects this transition toward production AI and autonomous agents. Developed using research from thousands of real-world AI security incidents, the guide provides practical explanations and mitigations for developers, architects, security teams, and CISOs. Excessive Agency ranks third in the 2026 edition, underscoring the need to control what AI systems can access, decide, and do.

6. Shadow AI is creating a data-governance problem

Employees often adopt public AI tools before security and governance teams can evaluate them. Sensitive source code, customer information, contracts, credentials, or internal documents may then be submitted to unapproved services.

Verizon reported that frequent employee use of AI tools rose from 15% to 45% in one year, making shadow AI the third most common form of non-malicious data leakage in its 2026 analysis.

Organizations can reduce this risk by maintaining an inventory of approved AI tools, establishing clear rules for the data employees may enter, providing sanctioned alternatives, and incorporating AI use into security awareness training. Technical controls may also help identify unapproved tools, but they should be paired with practical guidance that enables employees to use AI safely.

7. AI governance is converging with cybersecurity governance

AI risk is increasingly being incorporated into established security and risk-management programs instead of being treated as a separate discipline.

In December 2025, NIST released an initial preliminary draft of its Cybersecurity Framework Profile for Artificial Intelligence, also known as the Cyber AI Profile. It organizes the intersection of AI and cybersecurity around three priorities: securing AI system components, using AI for cyber defense, and thwarting AI-enabled attacks. The profile maps these priorities to the NIST Cybersecurity Framework 2.0, making it easier to incorporate AI into existing governance processes.

The EU AI Act has also entered a significant implementation phase. Its general-purpose AI obligations began applying in August 2025, while its Article 50 transparency obligations began applying on August 2, 2026. These requirements cover certain AI-generated or manipulated content and interactions with AI systems.

The Digital Omnibus on AI, which entered into force on July 27, 2026, extended the deadlines for high-risk systems. Requirements for standalone high-risk AI systems now apply beginning December 2, 2027, while requirements for high-risk AI embedded in regulated products apply beginning August 2, 2028. Organizations operating in the EU should determine which requirements and transition provisions apply to their systems.

Other resources, including the NIST AI Risk Management Framework, CISA’s Roadmap for AI, OWASP guidance for LLM and agentic applications, and MITRE ATLAS, can help organizations manage the security of their AI systems and prepare for AI-enabled threats.

AI and cybercrime

While AI is being applied in many ways to improve cybersecurity, it is also being used by cyber criminals to launch increasingly sophisticated attacks at an unprecedented pace.

AI does not have to introduce entirely new attack techniques to alter the threat landscape. Its immediate impact comes from making familiar activities such as reconnaissance, phishing, vulnerability research, malware development, credential theft, and stolen-data analysis faster, cheaper, and easier to scale.

IBM’s 2026 research illustrates how these capabilities are being used. Deepfake impersonation accounted for 45% of the AI-enabled attacks it studied, while AI-enabled malware and AI-generated phishing each accounted for 19%. Threat actors can also use AI to research vulnerabilities, search for exposed credentials, analyze stolen information, and coordinate activity against multiple targets.

In November 2025, Anthropic disclosed an AI-orchestrated cyberespionage campaign that it attributed with high confidence to a Chinese state-sponsored group. The campaign targeted roughly 30 organizations and resulted in a small number of successful intrusions. According to Anthropic, AI performed 80 to 90% of the campaign, including reconnaissance, exploit development, credential harvesting, backdoor creation, and data exfiltration. Human intervention occurred only sporadically, at perhaps four to six critical decision points per campaign. The AI accessed security tools, often through Model Context Protocol, demonstrating how the same interoperability that benefits defenders can also help attackers coordinate complex operations.

This demonstrates how AI can change the economics of cybercrime. Activities that once required a coordinated team of specialists may increasingly be executed by smaller groups supervising automated systems.

Organizations that use AI and automation to enhance their cybersecurity capabilities will be better positioned to defend against this threat. However, AI-enabled defense must be paired with foundational security practices such as multifactor authentication, least privilege, network segmentation, vulnerability management, secure backups, and incident-response planning.

How is cybersecurity AI being improved?

In response to these emerging threats, cybersecurity AI is being continuously improved to keep pace with cybercriminals and adapt its capabilities over time.

Below are key ways in which cybersecurity AI is being improved.

1. Better training and security context

AI models are getting better training thanks to increased computation and training data size. As these models ingest greater amounts of data, they have more examples to learn from and can draw more accurate and nuanced conclusions from the examples they are shown.

For cybersecurity applications, model capability is only part of the equation. AI systems also need reliable information about an organization’s assets, identities, sensitive data, configurations, vulnerabilities, and controls. Without that context, an AI system may generate recommendations that sound plausible but do not reflect the organization’s actual environment or risk.

2. Advances in language processing technology

Thanks to increases in data resources and computing power, language processing technology has made significant advances in the past few years. Natural-language threat queries are now embedded in security platforms, enabling analysts to ask questions in plain language and receive summarized findings, suggested searches, and recommended actions.

These advances, including enhanced capabilities to learn from complex and context-sensitive data, will significantly improve cybersecurity AI tools that automatically generate step-by-step remediation instructions, threat intelligence, and other code or text.

3. Threat intelligence integration

Cybersecurity AI systems are being enhanced by integrating with threat intelligence feeds. This enables them to stay updated on the latest threat information and adjust their defenses accordingly.

The most useful systems combine external threat intelligence with internal context. A newly disclosed vulnerability, for example, becomes much more actionable when AI can determine whether the affected software exists in the organization, whether it is exposed to the internet, what data it can access, and which protections are already in place.

4. Stronger controls for AI agents

Developers are creating more effective ways to manage agent identities, isolate AI systems during testing, restrict sensitive functions, and monitor agent behavior. These controls are becoming more important as AI systems gain access to organizational data and operational tools.

Records of the instructions an agent received, the information it accessed, and the actions it performed can help teams detect misuse, investigate incidents, and verify that safeguards are working.

5. Continuous evaluation and adversarial testing

AI systems can behave differently as models, instructions, data sources, and integrations change. A one-time security review cannot establish that an AI application will remain secure.

Organizations are increasingly using continuous evaluations and AI red teaming to test for prompt injection, sensitive data leakage, unsafe tool use, excessive autonomy, and other emerging weaknesses. Testing should reflect the complete application and its integrations, including the underlying model.

Harnessing the power of AI in your cybersecurity strategy

AI is making cybersecurity faster on both sides. Attackers can use it to accelerate established techniques, while defenders can use it to connect fragmented security data, investigate threats, test controls, and respond more quickly.

Success will depend on more than deploying additional AI tools. Organizations need accurate, current information about their assets, sensitive data, identities, vendors, and control environment. Without that foundation, AI may generate and prioritize conclusions using incomplete information.

Organizations should maintain an inventory of the AI systems they use and understand which data and tools those systems can access. AI systems should also be included in vendor assessments, security testing, incident-response plans, and employee training. Teams should measure whether AI improves outcomes such as detection accuracy and remediation time.

Security teams should view AI as a force multiplier that remains subject to human accountability. Routine work such as alert enrichment, documentation, evidence collection, and basic remediation will become increasingly automated. Human expertise will remain vital for security architecture, governance, risk decisions, incident leadership, and actions that could materially affect the business.

This shift makes continuous security and compliance increasingly important. Point-in-time audits still provide valuable independent assurance, but they cannot serve as an organization’s only view of its security posture. Controls must be monitored between assessments so teams can identify configuration drift, access changes, missing evidence, and emerging vulnerabilities as they occur.

At Secureframe, we're committed to making these advancements accessible. Our AI capabilities help organizations strengthen their security and compliance programs while saving valuable time and resources:

• Comply AI for AI Security Assessments provides a standardized approach to assessing vendor AI risk.
• Comply AI for Vendor Risk Management extracts key information from third-party reports like SOC 2 reports to simplify vendor reviews.
• Comply AI for Remediation generates infrastructure-as-code guidance for failing cloud tests.
• Comply AI for Risk analyzes risk descriptions and suggests treatment plans and scoring.
• Comply AI for Policies helps teams draft and customize security and compliance policies.
• Trust AI for Questionnaire Automation drafts responses to RFPs and due diligence requests using approved organizational knowledge.
• AI Evidence Validation reviews documentation before an assessment to help reduce audit findings and exceptions.
• Secureframe’s hosted MCP server enables compatible AI assistants to access current compliance data and perform permitted actions. Access is limited by the connected user’s existing Secureframe permissions.

The organizations best positioned for the next phase of cybersecurity will combine AI-enabled speed with strong identity controls, secure-by-design development, continuous validation, and human accountability.