SecOps teams today are drowning in noise - endless alerts, growing attack surfaces, and a flood of manual investigations that stretch teams thin. Finding real threats quickly requires more than just more tools; it demands smarter, faster workflows that cut through the chaos.

By automating key parts of the SecOps pipeline - from phishing analysis to threat intelligence aggregation - Kindo helps security teams focus on what matters: finding and stopping real threats faster. Instead of stitching together point solutions or relying on static playbooks, Kindo gives you dynamic, AI-based workflows that adapt to your environment in real time.

In this guide, we’ll break down five SecOps use cases that deliver immediate value with Kindo. Each one shows how automation can transform your day-to-day operations - making your team faster, sharper, and more resilient.

1. Phishing Detection and Triage

Phishing emails remain one of the top vectors for attacks. When employees report suspicious emails, SecOps teams must quickly determine which ones are real threats. Manually reviewing email headers, links, and attachments can take hours per incident and introduces the risk of human error. Automating the review process with AI saves a massive amount of time, improves consistency, and helps catch attacks faster.

Workflow Steps (Phishing triage)

1. The first thing we’re going to do is create an API action in Kindo that connects to Gmail and pulls emails automatically from the designated “phish inbox.” This inbox is where employees forward suspicious emails for investigation.

2. Next, we’ll insert a Kindo LLM action to review the pulled emails. The LLM will check the headers, links, and attachments for phishing indicators such as suspicious sender domains, fake login pages, malware-looking file types, or urgent messaging tactics.

3. Now, we’ll create another LLM action to classify the emails based on the previous analysis. For anything suspicious, the email will be labeled internally as "Needs-Investigation." For anything clean, it will be labeled as "Safe-Reviewed."

4. Finally, we’ll generate a phishing triage report summarizing the results with an LLM action. The report will include the subject, sender, findings, and classification for each email, giving analysts a clean list to prioritize their investigation without needing to manually review every message.

Value of Automation

With Kindo, phishing triage becomes fast and consistent. Instead of manually reviewing each reported email, your workflow automatically fetches reports, analyzes them with AI, and flags suspicious messages for investigation. This reduces analyst workload and minimizes human error. The LLM quickly identifies potential threats such as suspicious senders, fake login pages, or malicious attachments, and dismisses harmless emails so real attacks are caught earlier. By codifying best practices for phishing review, Kindo ensures no suspicious report is missed. The process scales easily, with every message analyzed in the same way, making your response more reliable and freeing analysts to focus on complex investigations.

2. Suspicious OAuth App Detection and Investigation

OAuth apps are becoming a bigger target for attacks. Harmful or overly permissive third-party apps can access sensitive data without setting off usual security alerts. Attackers are now using OAuth consent flows to get around multi-factor authentication (MFA), steal data, or maintain access to systems. Checking authorized apps manually can be time-consuming and is often ignored by security teams. Kindo makes this process easier by automatically gathering a list of OAuth apps authorized in your organization, analyzing risky permission levels, and flagging suspicious apps for investigation before they can cause harm.

Workflow Steps (Suspicious OAuth App Detection)

1. Set up a Kindo API action to pull metadata about OAuth applications from your identity provider. For example, use the Microsoft Graph API to retrieve service principal objects and OAuth2 permission grants in Azure AD, or use Google Admin SDK APIs to list authorized third-party apps in Google Workspace. This collects key fields such as app names, publishers, scopes requested, and the users who authorized them.

2. Add a Kindo LLM action to process the retrieved OAuth app metadata. The LLM reviews each app's publisher, requested scopes, and authorization patterns. It flags apps that request sensitive permissions (such as "read mailbox" or "modify calendar") or originate from unknown publishers. For each app, the LLM assigns a suggested risk level: high, medium, or low, depending on the scope sensitivity and publisher trustworthiness.

‍

3. Add another LLM step that formats the summarized risk findings into  clean .txt or .csv format. Group apps by risk level, and include details such as app name, publisher, requested permissions, authorized users, and assigned risk rating. The report is designed for easy review by analysts and can be attached to incident investigations or used to drive deauthorization efforts for risky apps.

Value of Automation

Manually inspecting OAuth authorizations across cloud environments is a slow, error-prone task that rarely keeps pace with growing application sprawl. By automating OAuth app discovery and risk analysis with Kindo, security teams gain immediate visibility into third-party access across the organization. Instead of combing through service principals and guessing which permissions are dangerous, analysts receive a clear risk summary that highlights suspicious apps for investigation. This proactive approach matches modern SecOps best practices, helping teams reduce exposure to OAuth-based attacks without adding operational overhead. As your cloud usage grows, Kindo’s agentic workflows continue to monitor new app authorizations, ensuring sustained visibility across an often-overlooked attack vector.

3. Insider Threat Indicator Aggregation

Insider threats, whether intentional or accidental, are hard to spot because the warning signs are often small and spread across different systems. For instance, an employee who is leaving might start downloading important files, logging in at unusual times, or submitting resignation notices. Kindo helps by automatically gathering these scattered clues into one place. By combining these signals, Kindo makes it easier to detect insider threats sooner and more reliably.

Workflow Steps (Insider Threat Monitoring)

1. Set up a Kindo API action to retrieve recent HR signals from systems like Workday, BambooHR, or a custom HR database. Focus on key employee status changes such as resignation notices, role changes, disciplinary actions, or terminations. This HR context provides important triggers for insider threat monitoring.

2. Create additional API actions to pull recent identity and access activity. For example, pull authentication data from AWS CloudTrail, Azure AD sign-in logs, or endpoint EDR tools. Also query file activity systems such as Box, Dropbox, or AWS S3 access logs. For the purposes of this guide, we’re going to focus on querying Azure AD sign-in logs to keep the workflow simple, but in practice, the more systems you include, the stronger your monitoring becomes. The goal is to collect any activity that could indicate risky behavior by users flagged through events. If your organization primarily uses email, then you would focus on pulling all relevant email access and audit logs instead. 

3. Pass all retrieved HR and activity data into a Kindo LLM step. The LLM analyzes the signals together, correlating behaviors like after-hours access combined with large file downloads after a resignation notice. It summarizes the findings in plain language and suggests a risk level (e.g., high, medium, or low) for each user based on the severity and combination of signals detected.

4. Finally add an LLM step to format the summarized findings into a clean .txt or .csv report. Group employees by their assigned risk level and clearly present the correlated indicators supporting each risk decision. The report is ready for review, escalation, or case management intake.

‍

Value of Automation

Manually identifying insider threats requires cross-referencing disparate systems like HR databases, identity providers, and file storage logs – an extremely tedious and error-prone process. Kindo automates this aggregation by continuously pulling and correlating signals across systems in one unified workflow. Analysts no longer have to bounce between sources or rely on intuition to piece together risky behavior. Instead, they receive structured summaries that surface potential insider risks early. By automating the collection, correlation, and reporting of insider threat indicators, Kindo reduces manual effort, increases consistency, and scales easily as your workforce and cloud footprint grow. This accelerates detection timelines and enables more proactive response to risky employee behavior.

4. Threat Intelligence Feed Aggregation

Threat intelligence (TI) feeds - lists of malicious IPs, domains, and file hashes - are a key input for SecOps. But with dozens of feeds (open-source and commercial), maintaining an up-to-date consolidated list can be overwhelming. Kindo automates this by pulling feeds from multiple sources, deduplicating and structuring the indicators in one place.

Workflow Steps (TI Feed Aggregation)

1. Set up a Kindo API action step to call the VirusTotal Intelligence API. Specify the IP address, domain, or file hash you want to enrich. This retrieves reputation and detection information, providing early context on potential threats.

2. Next, create another API action step to call the AbuseIPDB API. Set the IP address to query during setup. AbuseIPDB returns abuse confidence scores and reporting history for the selected indicator, helping assess malicious behavior.

3. Now, use a Kindo LLM step to normalize the enrichment results. The LLM validates formats, removes duplicates, and organizes indicators into a consistent structure.

4. Set up a LLM step to analyze and group the indicators. The LLM can flag high-risk entries, highlight overlaps between feeds, and organize IoCs by severity or threat type.

5. Finally, configure the workflow with an LLM step to output structured .csv or .txt format containing the finalized, enriched indicator list. Analysts can review, share, or manually import the report into downstream security tools.

Value of Automation

Automated feed aggregation keeps your threat intel fresh and comprehensive without manual effort. Instead of updating spreadsheets or individually checking each feed, Kindo continuously pulls selected sources, centralizes the data, and formats it for analyst use. Each API action is configured with specific indicators of compromise at workflow setup, providing clear, consistent enrichment results without manual searching. While this example uses VirusTotal and AbuseIPDB, Kindo’s flexible API step design allows teams to expand and include additional threat intelligence sources as needed, depending on their environment or threat ecosystem.

5. Cloud Security Anomaly Detection

Cloud environments are highly dynamic, and threats often manifest in subtle deviations across authentication, access, and system behavior. Manual rule-writing often fails to catch novel attacks or insider threats. A more effective approach is automated anomaly detection: analyzing security events to flag deviations from expected behavior profiles.

Workflow Steps (Cloud Anomaly Detection)

1. Set up an API action in Kindo to retrieve security-relevant logs from your cloud provider. This could include authentication activity and API invocation events from AWS CloudTrail, system and application audit logs from AWS CloudWatch, Azure Monitor, or GCP Cloud Logging.

2. Insert a Kindo LLM action to analyze the pulled logs for behaviors that deviate from established norms. The LLM reviews security events to flag patterns such as spikes in authentication failures, new geographies accessing sensitive services, unusual creation of service accounts, or unauthorized attempts to escalate privileges.

3. Finally, structure the summarized anomalies into a clean, formatted report with an LLM step. Grouping anomalies by event type such as authentication anomalies, API misuse, or privilege abuse. The report can be downloaded for forensic investigation, escalated through incident response processes, or integrated into case management systems.

Value of Automation

Automated anomaly detection in cloud environments dramatically shortens the mean time to detect (MTTD) potential threats. Instead of depending solely on rigid rulesets, Kindo pulls real-time cloud security activity and applies LLM-driven analysis to surface authentication anomalies, privilege escalation attempts, and other high-risk behaviors. While the system analyzes recent snapshots rather than maintaining full historical baselines, it enables proactive threat hunting across expanding cloud workloads. By surfacing indicators of compromise, suspicious access patterns, and unauthorized activity, Kindo accelerates forensic investigation and reduces the window of exposure. As your cloud footprint scales, Kindo workflows provide consistent, always-on anomaly monitoring without adding operational burden to security teams.

Get Started with Kindo

These examples highlight just a few of the powerful SecOps workflows you can automate with Kindo. Whether it’s improving phishing response, managing OAuth permissions, or detecting insider risks early, Kindo gives your team the tools to move faster and respond smarter.

Kindo’s platform connects your security tools and data sources through simple API integrations, then layers intelligent automation and AI-based analysis on top. You can quickly build playbooks that standardize best practices, adapt to new threats in real-time, and reduce the manual burden on your analysts.

Ready to unlock these benefits in your own environment? Explore what you can build with Kindo and see how agentic automation can transform your SecOps operations. Get a demo and take the next step toward smarter, faster security.

‍