DevOps teams are under constant pressure to maintain reliable systems and secure environments. From provisioning infrastructure to responding to incidents, they juggle a broad range of tasks – many of which are ripe for automation. Common workflows include:

1. Infrastructure as code checks and enforcement – verifying that Terraform, CloudFormation, and other IaC scripts are secure and compliant.

2. Vulnerability scanning and patching – identifying and prioritizing software vulnerabilities across servers, containers, and applications.

3. Compliance auditing and configuration monitoring – ensuring cloud and on-prem infrastructure adheres to security policies and standards.

4. Incident detection and response – gathering data and reacting quickly to security incidents or outages to minimize impact.

5. Access and IAM role reviews – auditing user privileges, roles, and keys to enforce least privilege and eliminate risky access.

These are foundational parts of modern DevSecOps, but they’re time-consuming, error-prone, and often stitched together manually across disparate tools.

This is where Kindo provides immediate value.

By using Kindo to automate DevOps workflows, you can connect your infrastructure and security tools, leverage AI to analyze configurations or logs, and create end-to-end playbooks that eliminate grunt work.

The result? Less time firefighting and fixing issues by hand, and more time innovating and improving reliability.

Kindo’s approach is to introduce AI-driven, agentic automation that adapts to your environment in real-time:

1. Infrastructure as Code Security Scanning

Maintaining infrastructure as code is a DevOps best practice, but misconfigurations in IaC files (Terraform, CloudFormation, Kubernetes manifests, etc.) can introduce serious vulnerabilities. Checking these files for security issues is usually a manual code review step or done with separate scanning tools, which might generate lengthy reports. It’s easy to overlook issues like open network ports, weak encryption settings, or overly permissive roles embedded in IaC. Automating this process ensures that every change is vetted against security baselines before it ever reaches production.

Workflow Steps

1. Start by setting up a Kindo workflow agent that triggers whenever new infrastructure code is pushed or on a schedule. For example, a trigger could check for any GitHub pull requests with the description “Terraform”.

2. Add an API action step to run an IaC security scanning tool. This could call a scanner’s API, like Terrascan, on the latest Terraform files. The agent will gather results about misconfigurations (e.g., an S3 bucket defined without encryption or a security group open to 0.0.0.0/0).

3. Next, include an LLM action step where Kindo’s AI analyzes the scanner output. The AI can filter out false positives and highlight the most serious issues. For instance, if dozens of issues are reported, the LLM could summarize: “3 high-priority risks found: an open SSH port on a web server, an IAM role with wildcard permissions, and an unencrypted database storage.” This focuses the team’s attention on what matters most.

4. Optionally, add another LLM step to suggest remediation for each issue. Kindo’s agent can be prompted to generate code patches or recommendations. For example, if a security group rule is too open, the agent might suggest restricting it to known CIDR ranges or referencing a parameter for allowed IPs.

Value of Automation

By integrating IaC checks into a Kindo workflow, you get immediate feedback on infrastructure risks before deployment. This saves time compared to manual reviews and ensures consistency – no configuration drift or risky setting slips through due to human error. The automation is repeatable and scalable: every Terraform plan or Kubernetes manifest goes through the same rigorous scrutiny. This proactive catching of issues can prevent costly incidents down the line (for example, avoiding an S3 bucket left open to the public).

Moreover, your DevOps engineers are freed from combing through security scan reports; instead, they receive a concise summary and even ready-made code fixes. The process becomes not only faster but more reliable, as automation reduces the chance of overlooking misconfigurations. In short, Kindo turns a tedious yet crucial task into a smooth, continuous mechanism in your pipeline.

2. Automated Vulnerability Management & Patching

Keeping systems up-to-date with the latest security patches is an ongoing battle. New CVEs (Common Vulnerabilities and Exposures) are disclosed daily, and DevOps teams must triage which ones affect their stack, which are critical, and how to remediate them. Typically, this involves running vulnerability scanners (for OS packages, containers, open source dependencies) and manually correlating their outputs with asset inventories and patch availability. Important issues can be missed or languish unpatched due to the noise. Kindo can drastically accelerate this workflow by acting as a tireless vulnerability management assistant that continuously scans and prioritizes fixes.

Workflow Steps

1. Configure a Kindo agent to periodically fetch vulnerability data. For example, the agent might call an API for your vulnerability scanner (Nessus, Qualys, or open-source tools like OpenVAS). It could also query databases like NVD or vendor security advisories for the latest critical CVEs. The key is gathering a list of vulnerabilities relevant to your environment – e.g., CVEs for software you run or images in your container registry. In the example below we’re querying Nessus for scan results.

2. Next, the agent performs an analysis to cross-reference vulnerabilities with your systems. You might include an API step to a configuration management database or cloud inventory to get a list of software versions in use. Below we are querying AWS Systems Manager Inventory as an example.

3. Add an LLM step to prioritize the vulnerabilities. The agent can assess each finding’s criticality (CVSS score, exploit availability) and context (is the affected system internet-facing?)

4. Finally, Kindo can assist in remediation. The last step could use the LLM to suggest a patch or mitigation for each high-priority vulnerability. For instance, it might output: “Upgrade OpenSSL to version 1.1.1x or later to fix CVE-2025-1234” or “Apply the vendor patch KB12345 for Windows Server to address these CVEs.”

Value of Automation

This workflow turns vulnerability management from a periodic scramble into a continuous, optimized process. Instead of security engineers combing through scanner reports and manually mapping them to systems, the Kindo agent does the heavy lifting in minutes. Automation ensures that no critical vulnerability slips through the cracks due to oversight. It also standardizes how issues are evaluated – the same criteria are applied every time to assess risk and urgency, reducing the subjectivity or inconsistency that can happen with human triage.

By focusing attention on the vulnerabilities that truly matter, teams can remediate faster, often shortening the window of exposure by days or weeks. An automated agent can track threats 24/7, so the moment a new high-impact CVE is announced, it can determine if you’re affected and alert you with a recommended action plan. This immediate awareness and guidance significantly reduces the mean time to patch. In sum, Kindo helps DevOps and DevSecOps teams stay one step ahead of vulnerabilities, achieving a more secure posture with much less manual effort.

3. Continuous Compliance and Configuration Audit

In highly regulated or security-conscious environments, compliance checks and configuration audits are a regular necessity. DevOps and Platform Engineering teams often need to verify that systems conform to standards like CIS benchmarks, GDPR data policies, or internal security guidelines. Manually auditing dozens of configuration settings – from S3 bucket policies to VM encryption to Kubernetes pod security – can be a laborious process. A lot of organizations rely on periodic audits or a patchwork of cloud security posture management (CSPM) tools, which might produce static reports that quickly go out of date. With Kindo, you can build a continuous compliance agent that keeps a watchful eye on your infrastructure’s state and flags deviations in real-time.

Workflow Steps

1. Set up a Kindo workflow agent that routinely (e.g., daily or weekly) pulls configuration data from your environment. This could involve multiple API action steps: for example, one step calls AWS Config or AWS API to list resources like S3 buckets, security groups, and IAM policies; another step hits Azure Security Center or GCP’s APIs for their resources; another could pull Kubernetes cluster settings. Essentially, the agent collects all the relevant settings and resource configurations across your stack. For the purpose of this guide, we’re going to keep it simple and query AWS for all IAM policies.

2. Prepare a set of compliance criteria or best practices for the agent. This could be done by storing rules in a file or simply encoding them in the prompt of an LLM action. For instance, you might include rules such as “All S3 buckets must have versioning and encryption enabled,” “No security group inbound rule should allow 0.0.0.0/0 on any port except 80/443,” “Databases must have backups enabled,” and so on.

3. In the final step, the agent can generate a compliance report. This might be another LLM action formatting the findings into a concise summary (grouped by severity or compliance requirement). For critical violations, you might configure the agent to send an immediate alert. For example: “Alert: Non-compliant configuration – Public S3 bucket test-data found (owner: DataScience team).” Each alert or report entry would include recommended fixes, which the LLM can provide (“enable bucket encryption and restrict access to VPC XYZ”).

Value of Automation

A continuous compliance agent ensures that security and policy adherence isn’t a one-time or ad-hoc activity, but an ongoing process. This delivers value by drastically reducing the time and effort needed to spot configuration drift or policy violations. Instead of waiting for a quarterly audit (and discovering a pile of issues), teams get near-instant feedback whenever something goes out of bounds. The workflow can check many data sources in parallel – something that would take an individual engineer hours of clicking through consoles or running scripts across accounts. With Kindo, this might run in minutes and cover your entire cloud footprint.

Automation also improves accuracy: it applies the same set of compliance rules uniformly, eliminating human oversight errors and ensuring nothing is missed due to fatigue or oversight. Importantly, by catching misconfigurations early, you reduce risk – the window of vulnerability (e.g., a storage bucket left open or an overly permissive firewall) is minimized. This not only hardens security but also keeps you continuously audit-ready. Should an external compliance audit occur, you can confidently demonstrate that you have an active system in place monitoring and enforcing your controls.

4. Incident Response and Root Cause Analysis

No matter how well we automate and secure, incidents are a fact of life for anyone managing complex systems. Whether it’s a security breach indicator or a sudden drop in a service’s reliability, responding quickly and effectively is paramount. Traditional incident response often means scrambling: pulling logs from servers, querying monitoring systems, checking recent deployments, and so on. Many organizations have tried to script parts of this (using SOAR runbooks or custom scripts), but static workflows fall short when an incident doesn’t match a predefined pattern. Kindo’s agentic approach – using an AI-driven workflow that can adapt on the fly – is a game-changer for incident response. It’s like having a junior SRE/SecOps analyst available 24/7, gathering evidence and even suggesting fixes, so your experts can validate solutions faster.

Workflow Steps

1. First, decide how the Kindo incident response agent is invoked. It could be triggered by an alert from your monitoring stack or SIEM (for example, a CloudWatch Alarm, Datadog alert, or Splunk correlation search firing), or manually triggered by an on-call engineer who notices something wrong. For the purposes of this guide, we’re going to trigger upon ticket creation to a specific user through Kindo’s Jira integration capability.

2. Once activated, the agent can immediately begin collecting relevant data. One API step might query your APM tool (like New Relic or Grafana Loki) for error rates and traces around the incident timeframe. Yet another could fetch recent AWS CloudTrail events or security logs if a security anomaly (like AWS GuardDuty finding) triggered the workflow. Essentially, the agent aggregates all the context: log lines, metrics, user sessions, recent code deployments, etc., that might be relevant to diagnosing the issue. Below we will query New Relic for incidents.

3. With data in hand, include an LLM action step for analysis. The AI will sift through the collected information to identify root cause hypotheses or indicators of compromise. For example, it might correlate an application error from the logs with a configuration change made 30 minutes before, concluding that a misconfiguration caused a crash. Or in a security scenario, it might spot that a particular IP address accessed an unusual number of resources before the alert, and note that IP had a known malicious reputation (perhaps by also cross-checking a threat intel API). The LLM can summarize findings: “Root cause identified: recent deployment of version 2.3 introduced an invalid database connection string, leading to repeated exceptions.” Or “Security Incident: multiple failed login attempts followed by a successful admin login on host X, originating from foreign IP 123.45.67.89 – likely compromised credentials.” This kind of reasoning – across disparate data sources – is where the AI shines, as it’s not limited to a preset playbook but can truly adapt to the scenario in real-time.

4. As a final step, the agent can generate an incident report. Using the LLM, it can compile a timeline of what happened, and the identified root cause. This report could be stored in your knowledge base or sent to stakeholders, ensuring no detail is missed. Such documentation is valuable for post-incident reviews and for training the team on how to prevent or handle similar issues in the future.

Value of Automation

This incident response workflow brings immediate relief to on-call teams. By automating the data collection and initial analysis, it cuts down the critical minutes (or hours) spent figuring out what’s going on. Teams often describe incident response as “finding needles in haystacks” – Kindo effectively uses AI-powered magnets to pull out those needles (the key indicators) from multiple haystacks at once. The adaptability of the agent means even if you encounter a novel issue, it can intelligently navigate the investigation rather than hitting a dead-end; you’re not limited to pre-scripted checks.

Moreover, the consistency and thoroughness of an automated agent means fewer things are missed during an investigation, leading to more complete fixes (so you don’t get repeat incidents). Finally, having a documented response each time helps build organizational knowledge. Your DevOps and SRE teams can focus more on preventive work and improvement, rather than constantly fighting fires.

5. IAM and Cloud Role Analysis

In cloud-centric infrastructure, IAM plays a pivotal role in security. Permissions and roles determine what each user, application, or service can do. Over-provisioned privileges, orphaned accounts, or misconfigured roles are a major security risk – they could be exploited by insiders or external attackers to gain unauthorized access. However, reviewing IAM policies is tedious: a large AWS environment might have hundreds of roles and policies, each a JSON blob of actions and resources. Manually auditing these for least-privilege adherence or unused permissions is like searching for needles in a stack of needles. Kindo can serve as an automated IAM audit assistant, intelligently analyzing permissions to surface the riskiest configurations for you.

Workflow Steps

1. Configure a Kindo agent with access to your identity management APIs. For AWS, an API action step can use AWS SDK calls to list all IAM users, roles, and their attached policies. Similarly, for Azure or GCP, it can list service principals, role assignments, etc. The agent could also retrieve user activity logs (e.g., AWS CloudTrail events or Azure AD sign-in logs) to see how permissions are being used in practice. Below, we are going to query AWS to see all IAM users and settings.

2. Add an LLM action step where the agent analyzes the gathered IAM data. This step can be prompted with instructions to detect things like: roles with wildcard ("*") permissions, accounts with admin privileges, credentials that haven’t been used in a long time, or any deviations from least-privilege principles. The agent effectively combs through the raw policies and usage data to highlight where your access controls are too lax or potentially unused and ripe for removal. (Notably, Kindo’s dynamic analysis is powerful here – it’s not hard-coded rules, but it can understand the intent of policies and usage patterns.)

3. In the final step, the agent produces an IAM risk report. Using another LLM action, it can generate a human-readable summary of the findings, perhaps ranked by risk level. For each issue, the agent can include a recommended remediation. The report might also map findings to compliance requirements (e.g., alert that certain IAM configurations violate a compliance policy that requires MFA or prohibits wildcard permissions).

Value of Automation

Automating IAM and access reviews gives you quick wins in strengthening your cloud security posture. The immediate benefit is visibility – you get a clear map of where your biggest identity and access risks are, something that’s nearly impossible to compile manually in a reasonable time. Instead of security engineers painstakingly reading policy documents (and often delaying such audits due to the effort required), the Kindo agent can do it continuously and objectively. It will catch subtle issues, like a policy that grants an unnecessary s3:ListAllMyBuckets permission to an application role, which a human might shrug off but could be tightened.

By identifying unused credentials or roles, the agent also helps with hygiene cleanup – removing those reduces your attack surface straight away. Many breaches and cloud security incidents stem from overly generous permissions or forgotten accounts; this workflow targets exactly those. The time saved is huge: consider that an engineer might spend days every quarter doing an IAM audit for a large environment, whereas an automated agent could perform a basic check daily. This means issues are flagged immediately when they appear (e.g., someone accidentally attaches an admin policy to a service account – you’d know within a day and can fix it). It also means audits no longer bog down your team.

Accelerating DevOps with Kindo’s Agentic Automation

Ready to unlock these benefits in your own environment?

It’s time to explore what Kindo’s adaptive automation can do for your team. If you’d like to see how an AI-driven agent can revolutionize your DevSecOps processes – without forcing you to script every possibility – reach out for a demo or trial.

Kindo’s autonomy, on-premise deployment, and human-in-the-loop oversight combine to handle both routine tasks and the curveballs that traditional workflows can’t catch.

Take the next step toward smarter DevOps automation and let Kindo augment your operations – so you can spend less time on tedious tasks and more time innovating on what truly matters.

https://go.kindo.ai/get-demo

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