Generative AI Use Cases in Security Operations — From SOC Analysis to Cloud Security

The notion that generative AI will replace security professionals is an exaggeration. However, it is clear that it is a tool that reduces a significant portion of security tasks such as reading, summarizing, comparing, drafting, and repetitive lookup operations.

The data that security teams face daily is vast.

Hundreds of security alerts
Long and complex logs
Obfuscated scripts
Newly disclosed vulnerabilities and threat intelligence
Cloud and Kubernetes configuration files
Security policies, audit results, and incident reports

The problem is not a lack of information, but rather too much of it, making it difficult to find critical signals in a timely manner. Generative AI is particularly useful for transforming this information into a format that is easy for humans to interpret.

This article explores where generative AI can be applied in actual security operations, rather than focusing on product introductions. For each use case, we will outline what can be delegated, what results can be expected, and what humans need to verify.

Generative AI organizing security alerts and cloud information into actionable insights for analysts.

What Generative AI Excels at in Security Operations

The strength of generative AI is not its ability to automatically detect all attacks. It is its ability to organize disparate forms of information and create the context necessary for humans to make subsequent judgments.

Task Type	What Generative AI Excels At	What Humans Must Do
Summarization	Organizing long alerts and logs into an incident timeline	Verifying consistency with original evidence
Conversion	Converting natural language into KQL, SPL, SQL, Sigma drafts	Validating query scope, fields, and performance
Classification	Suggesting priorities for suspicious phishing emails or vulnerabilities	Judging false positives/negatives and business impact
Explanation	Explaining obfuscated code, attack techniques, and security configurations	Verifying in sandbox and real environments
Drafting	Writing incident reports, detection rules, and response procedures	Approval, responsibility judgment, and final execution
Comparison	Analyzing differences between policies, configurations, and versions	Checking against latest official documentation and operational conditions

The key is to position generative AI not as the final arbiter, but as an analytical assistant.

Use Case 1: Summarizing Security Alerts and Incident Timelines

SOC analysts must navigate endpoint, email, account, firewall, and cloud logs to investigate a single incident. Multiple products may also generate different alerts for the same event.

Generative AI can organize the following data into a single chronological sequence:

Initial detection time
Related users and devices
Suspicious IPs, domains, file hashes
Executed processes and commands
Attack stages such as initial access, privilege escalation, and lateral movement
Unanalyzed evidence and additional verification items

For example, you can request it as follows:

아래 경보와 로그를 시간 순서로 정리해줘.

1. 확인된 사실과 추정을 분리한다.
2. 사용자, 호스트, IP, 프로세스 관계를 표로 만든다.
3. MITRE ATT&CK 전술과 기법은 근거가 있는 경우에만 연결한다.
4. 누락된 조사 항목을 질문 형태로 제시한다.
5. 격리나 차단 작업은 실행하지 말고 제안만 한다.

Microsoft’s official Security Copilot examples also demonstrate a workflow that summarizes incidents involving multiple alerts, explains attack stages, and provides response guidance.

However, the timeline generated by AI is not the original event. The final report must include SIEM event IDs, timestamps, queries, and original evidence.

Use Case 2: Alert Prioritization and False Positive Analysis

Processing all alerts in the same order can cause critical incidents to be delayed. Generative AI can help suggest priorities that include organizational context, rather than just the severity of the alert itself.

For example, you can input the following information:

Alert type and detection rule
Importance of the targeted asset
Internet exposure status
User permissions and roles
Presence of known vulnerabilities
Recent change operations
Past alerts for the same entity

The results are better presented in the following format rather than simply categorizing them as risky or safe:

Judgment Item	Output Example
Provisional Priority	Urgent, High, Medium, Low
Basis for Judgment	Confirmed logs and asset importance
Uncertainty	Missing data and conflicting signals
Further Inquiry	Required SIEM/EDR queries
Recommended Actions	Analysis, isolation review, user verification

AI alerts should not be automatically closed simply because AI classifies them as low risk. Especially for administrator accounts, domain controllers, CI/CD credentials, and externally exposed systems, separate conservative policies are required.

Use Case 3: Threat Intelligence Summarization and IOC Enrichment

When new attack campaigns or CVEs are disclosed, security professionals must read various reports and determine their relevance to their environment.

By providing generative AI with official advisories and reliable threat intelligence, the following tasks can be performed quickly:

Summarizing attacker objectives and key TTPs
Extracting IPs, domains, URLs, and hashes from IOCs
Mapping to MITRE ATT&CK techniques
Organizing affected products and versions
Generating questions to cross-reference with our asset inventory
Drafting detection and mitigation measures

첨부한 공식 권고문에서 확인된 사실만 사용해 다음을 작성해줘.

- 영향받는 제품과 버전
- 악용 전제조건
- 침해지표 목록
- MITRE ATT&CK 연결과 근거 문장
- 우리 환경에서 확인할 자산·로그·쿼리
- 패치 전 임시 완화책

문서에 없는 내용은 추측하지 말고 '확인 필요'로 표시해줘.

What is important here is source traceability. Do not just save the AI’s summary; you must also preserve the original URL, publication date, CVE number, and the basis for the citation.

Use Case 4: Drafting Threat Hunting Queries with Natural Language

SIEMs are powerful, but their query languages differ across products. It is difficult to master KQL for Microsoft Sentinel, SPL for Splunk, general SQL, and EDR-specific syntax all at once.

Generative AI is useful for converting natural language requirements into query drafts and explaining existing queries.

Microsoft Defender Advanced Hunting용 KQL 초안을 작성해줘.

목표: 최근 24시간 동안 Office 프로세스가 PowerShell을 실행한 사례 탐지
필수 출력: 시간, 장비명, 사용자, 부모 프로세스, 명령줄, SHA256
제외: 승인된 자동화 계정 sec-automation
주의: 실제 테이블과 필드가 없으면 임의로 만들지 말고 확인이 필요한 필드로 표시

Queries generated by AI should not be immediately registered as operational detection rules.

Verify schema and field names.
Execute with a short duration and limited data.
Compare result samples with original events.
Check query cost and execution time.
Review whether false positive exclusion conditions do not become an attacker’s bypass path.

A good approach is not to fully delegate query generation, but to quickly obtain a verifiable first draft for analysts.

Use Case 5: Analyzing Suspicious Scripts and Command Lines

PowerShell, Bash, JavaScript, and macros are used for both attacks and legitimate automation. Generative AI helps in breaking down long command lines, explaining the meaning of each option, and identifying obfuscation layers.

Possible tasks include:

Identifying Base64 and URL encoding
Explaining PowerShell execution arguments
Summarizing file download and execution flows
Extracting network connection targets
Identifying persistence mechanism candidates
Suggesting files, registry entries, and processes to investigate

However, AI should not be allowed to directly execute the code under analysis. Decrypted scripts must be statically and dynamically analyzed in an isolated sandbox, and even code described as safe by AI should not be trusted.

Furthermore, actual malware samples, logs containing customer information, or internal paths should not be uploaded directly to public AI services. They should be used within the organization’s approved security boundaries or sensitive information must be removed.

Use Case 6: Drafting Detection Rules and Sigma Rules

After identifying new attack techniques, they must be converted into detectable conditions. Generative AI can transform analytical statements into a first draft of Sigma rules.

다음 행위를 탐지하는 Sigma 규칙 초안을 작성해줘.

- Windows에서 rundll32.exe가 원격 URL을 인수로 사용
- 부모 프로세스와 전체 CommandLine 포함
- ATT&CK 매핑에는 근거를 주석으로 남김
- 정상 관리 도구와 충돌할 수 있는 오탐 사례 제시
- 운영 적용 전 필요한 로그 소스와 감사 정책 명시

The items to review are clear:

Are the necessary logs actually being collected?
Do the field names match the target SIEM converter?
Does it handle case sensitivity and path variations?
Does it not rely on a single string that attackers can easily bypass?
Does it not conflict with legitimate software deployments?

AI can reduce rule creation time, but detection quality is determined by test data and continuous tuning.

Use Case 7: Vulnerability Prioritization and Patch Impact Analysis

In vulnerability management, simply addressing items with high CVSS scores is insufficient. Actual risk depends on asset exposure and importance, exploitability, and compensating controls.

Generative AI can organize the following information into a single decision matrix:

CVE description and CVSS vector
Inclusion in CISA KEV (Known Exploited Vulnerabilities)
Vendor advisories and patch versions
Internet-exposed assets
Installed products and actual versions
Attack prerequisites and required permissions
Compensating controls such as WAF, network segmentation

Question	Generative AI Output
Is our environment affected?	Verification list by asset and version
Is it exploitable now?	Summary of prerequisites and exposure paths
Which targets to patch first?	Priority suggestions with rationale
If patching is difficult?	Summary of temporary mitigation measures based on official documentation
What to monitor?	Related logs and detection ideas

Patch commands or configuration changes must always follow official vendor documentation and change management procedures. This is because AI can generate non-existent patch versions or options.

Use Case 8: Cloud and Kubernetes Configuration Review

IAM policies, Terraform, Kubernetes YAML, and network policies are text-based, so generative AI can handle them relatively well.

For example, you can ask it to find the following issues:

Excessive IAM wildcard permissions
Public storage and internet exposure
Broad inbound rules in security groups
Privileged execution of containers
Use of hostNetwork, hostPID, hostPath
Missing securityContext
Hardcoding secrets in plain text
Excessive permissions in Kubernetes RBAC
Absence of NetworkPolicy

이 Kubernetes 매니페스트를 보안 관점에서 검토해줘.

각 항목을 위험도, 근거가 되는 YAML 경로, 공격 시나리오,
최소 변경 수정안으로 정리한다.
Pod Security Standards와 Kubernetes 공식 문서를 우선 기준으로 사용한다.
애플리케이션 동작을 알 수 없는 항목은 단정하지 말고 질문으로 남긴다.

Do not immediately kubectl apply or reflect AI-generated modifications in Terraform. They must pass schema validation, policy engines, test environments, and code reviews.

Use Case 9: Assisting Phishing Email Investigations

Phishing report emails often have many items that need to be repeatedly checked.

Display name and actual sender address
Reply-To mismatch
SPF, DKIM, DMARC results
URL and redirection chain
Attachment format and hash
Whether the user clicked the link
Subsequent login and session activity
Recipients within the organization from the same sender

Generative AI can summarize headers and security product results, and suggest further investigation steps. Microsoft also presents phishing triage, which classifies user-reported emails and provides analytical grounds, as an official security AI application area.

However, email bodies may contain personal information and company secrets. Before transmitting original content to external models, ensure it is an approved service, and if necessary, de-identify addresses, body content, and attachments.

Use Case 10: Incident Response Procedures and Report Generation

When an incident occurs, analysis and documentation must happen simultaneously. However, maintaining report quality in urgent situations is challenging.

Generative AI can draft the following documents based on confirmed evidence:

Incident overview
Chronological timeline
Affected assets and accounts
Confirmed scope of compromise
Isolation, blocking, and recovery actions performed
Remaining risks and additional tasks
Executive summary
Recurrence prevention tasks

A good input method is not to simply dump an analyst’s notes, but to distinguish between facts, assumptions, decisions, and execution results.

아래 사고 기록으로 보고서 초안을 작성해줘.

- 확인된 사실과 분석가의 추정을 분리한다.
- 시간은 모두 KST와 UTC를 병기한다.
- 수행하지 않은 조치를 수행했다고 쓰지 않는다.
- 근거 이벤트 ID가 없는 결론은 '검증 필요'로 표시한다.
- 기술팀용 상세본과 경영진용 5문장 요약을 각각 만든다.

Legal filings, customer notifications, and public statements should not use AI results without review by legal, privacy, and management stakeholders.

Use Case 11: Organizing Security Policies and Audit Evidence

Security professionals spend a lot of time not only on technical analysis but also on policy and audit responses. Generative AI can assist with the following tasks:

Comparing overlaps and conflicts across multiple policy documents
Mapping control items to actual evidence
Drafting interview questions and checklists
Translating technical configurations into language for non-technical stakeholders
Organizing deficiencies and required evidence lists

For example, when linking ISMS, ISO 27001, or internal control standards to cloud configurations, AI can create a mapping draft. However, AI should not make final judgments on certification compliance or legal interpretations.

Automation Levels Are Safely Divided into Three Stages

When adopting generative AI, it is better to start with lower-risk tasks rather than immediately creating automated response agents.

Stage	AI Authority	Suitable Tasks
Stage 1: Reading/Summarizing	Data lookup and draft generation	Alert summaries, reports, query explanations
Stage 2: Suggestion	Generating investigation queries and action plans	Hunting queries, detection rules, ticket creation
Stage 3: Limited Execution	Invoking approved tools within a narrow scope	Account lockout requests, device isolation workflows

Initially, quality and risk are measured in Stage 1. In Stage 2, execution occurs after human approval, and Stage 3 is applied only when strong permission restrictions, audit logs, and kill switches are in place.

Specifically, the following tasks should fundamentally require human approval:

Account deactivation and privilege revocation
Endpoint isolation
Firewall blocking
Kubernetes resource deletion
Cloud IAM changes
Notification to external entities or customers

Data That Security Teams Should Not Input into Generative AI

Before considering the benefits of adoption, data boundaries must be defined.

Data	Basic Processing Principle
Passwords, API keys, tokens	Prohibited input, immediate masking
Customer personal information	Process minimally in an approved environment
Internal source code	Use after checking contract, retention, and learning policies
Original malware	Process only in an isolated analysis environment
Incident logs	Review de-identification of accounts, IPs, hostnames, etc.
Undisclosed vulnerabilities	Prohibited input to public services, access control

NIST’s Generative AI Risk Management Guide addresses data privacy, information integrity, information security, human-AI role configuration, and third-party generative AI incident response as key risk areas.

Organizations should at least be able to answer the following questions:

Is input data used for model training?
How long are prompts and responses retained?
In which region is the data processed?
Can administrators and vendors view conversations?
Are there deletion and audit log functionalities?
What permissions do connected plugins and agents have?

Checklist for Verifying Generative AI Responses

In security operations, plausible but incorrect answers are the most dangerous.

Did it not generate events that are not in the original logs?
Did it not suggest non-existent CVEs, patch versions, or commands?
Are there sources for IOCs and attack techniques?
Did it distinguish between facts and assumptions?
Are the timestamps and event order correct?
Does the query execute on the actual schema?
Does the proposed action not cause business interruption?
Does it receive human approval before deletion, isolation, or blocking?
Are AI inputs, outputs, and execution records logged for auditing?

MITRE ATLAS systematically outlines how prompts, search contexts, tool connections, and control policies can become attack surfaces for generative AI and agent systems. When security teams utilize AI for defense, the security of the AI system itself must also be designed.

How to Start with a Small Pilot

For the first project, tasks that can be rolled back, such as drafting incident reports or summarizing alerts, are more suitable than automatic blocking.

Select one time-consuming, repetitive task.
Define the permissible data classification for input.
Prepare 20-50 past cases with known correct answers.
Measure accuracy, omissions, hallucinations, and reduction in task time.
Define human review and approval points.
Record prompts and results for auditability.
Expand to the next task after passing quality standards.

Evaluation metrics should also be more specific than simple satisfaction scores.

Reduction rate in analysis time
Rate of missing critical evidence
Rate of incorrect IOC/CVE generation
Query execution success rate
Percentage of sentences modified by analysts
Changes in false positives/negatives
Compliance rate for human approval of high-risk actions

Conclusion: Generative AI Reduces Decision Preparation Time Rather Than Replacing Decisions

In security operations, the areas where generative AI has the greatest impact are repetitive information organization and initial drafting.

Tasks such as transforming alerts into incident flows, extracting IOCs from threat reports, converting natural language into hunting queries, identifying potential risks in cloud configurations, and structuring incident reports are well-suited for generative AI.

Conversely, final judgments on attack status, system isolation, account blocking, patch application, and legal interpretations still require review by a responsible human.

The principle for good adoption is simple:

AI organizes evidence and suggests options. Humans verify the original and make responsible decisions.

Starting generative AI as an automatic arbiter for a security team increases risk. Starting it as an assistive tool to help analysts make faster and more consistent judgments makes it easier to measure and control its effectiveness.