
## About the Customer

A leading global IT services and business solutions provider serving **Fortune 500 enterprises** across multiple industries including finance, healthcare, telecommunications, and technology. The company operates a complex, distributed multi-cloud infrastructure spanning AWS, Microsoft Azure, and Google Cloud Platform, managing infrastructure and applications for hundreds of enterprise clients.

As a trusted IT services partner, the organization manages mission-critical workloads, sensitive customer data, and highly regulated compliance requirements across 800+ cloud accounts. Maintaining rigorous security posture and compliance is essential to client trust, regulatory adherence, and business continuity.

## The Challenge

As the IT services provider scaled their multi-cloud infrastructure to support growing client workloads, their security operations team faced mounting challenges in operationalizing security across 800+ accounts spanning multiple cloud providers. Key pain points included:

## Multi-Cloud Posture Management at Scale

Trend Micro Cloud One Conformity detected thousands of posture violations daily across 800+ AWS, Azure, and GCP accounts—IAM policy violations, storage encryption gaps, network misconfigurations, Kubernetes security issues, and compliance drift. Converting these findings into action was manual, slow, and error-prone. Teams reviewed CSPM reports, assessed violations, created remediation tickets, coordinated with infrastructure teams, and tracked resolution—often leaving critical issues unresolved for extended periods.

## Distributed Team Coordination Complexity

Unlike traditional enterprises with centralized infrastructure teams, the IT services company had distributed cloud infrastructure teams across different geographies managing specific client workloads and dedicated cloud accounts. Centralizing all security remediation through a small corporate security team created severe coordination delays. Infrastructure teams responsible for specific client environments couldn't make quick security decisions—everything required approval from central security, creating bottlenecks.


## Client Compliance and Audit Requirements

As an IT services provider, the company supported clients with varying compliance requirements (HIPAA for healthcare clients, PCI-DSS for financial services clients, SOC2 for SaaS clients, etc.). Managing compliance posture across 800+ accounts with multiple regulatory frameworks, while demonstrating audit-readiness to multiple client stakeholders, required exhaustive documentation and manual compliance tracking.

## Manual Remediation of Routine Tasks

Many CSPM violations were straightforward to fix—enable encryption on storage, rotate unused keys, add security group rules, enforce pod security policies. Yet each required manual investigation, ticket creation, stakeholder coordination, approval routing, and verification. Routine security work was consuming analyst time that should have been spent on strategic security improvements and client-focused security advisory work.

## Multi-Cloud Tool Fragmentation

Managing security across AWS, Azure, and GCP required separate tool integrations, different remediation procedures, and inconsistent approval workflows. Infrastructure and security teams were stitching together disconnected processes across multiple cloud platforms and client environments, creating gaps and compliance inconsistencies.


## Business Impact: Slow Infrastructure Deployment

Security reviews and remediation bottlenecks were slowing down client infrastructure deployments. Infrastructure teams waiting for security approvals meant delayed time-to-market for client projects, creating business friction and competitive disadvantage.


## The Solution

The customer deployed autobotAI as a unified workflow automation platform, integrated with Trend Micro Cloud One Conformity, to orchestrate intelligent security automation across their entire multi-cloud infrastructure serving hundreds of clients.

## Unified Multi-Cloud Integration Layer

autobotAI integrated with the customer's core tools:

**Trend Micro Cloud One Conformity** — continuous security posture monitoring across 800+ AWS, Azure, GCP accounts
**AWS, Azure, Google Cloud APIs** — cloud infrastructure visibility and remediation across client environments
**Kubernetes APIs** — container workload security across all cloud platforms and client clusters
**Internal CMDB** — cloud resource ownership, client associations, and infrastructure team metadata
**Slack** — notifications, approvals, and team collaboration across distributed teams


## 30+ Automated Security and Compliance Workflows
Working with the customer's infrastructure, security, and client management teams, autobotAI engineers built 30+ automation workflows covering critical security domains:


## IAM Remediation Automation for Multi-Client Environments

autobotAI continuously monitors for IAM policy violations, overly-permissive roles, orphaned service accounts, and unused access keys across all three cloud platforms and all client environments. The platform assesses each violation in context of the client's compliance requirements, determines appropriate remediation, and routes approval requests to infrastructure team owners based on resource and client metadata. Low-risk fixes are auto-remediated with audit logging. High-risk policy changes affecting client workloads require approval from responsible infrastructure teams before execution.


## Network Security Automation Across Client Infrastructure

Overly-permissive security groups, network ACLs, and firewall rules are automatically identified and remediated across AWS, Azure, and GCP. The platform applies consistent security policies while respecting cloud-specific requirements, client compliance needs, and routing critical network changes through appropriate approval workflows.


## Kubernetes Posture Automation for Container Workloads

Pod security policy violations, missing network policies, RBAC configuration gaps, and workload isolation issues are automatically detected and remediated across Kubernetes clusters deployed on all three cloud platforms and supporting multiple client projects.


## Multi-Compliance Framework Automation

Continuous monitoring ensures adherence to multiple compliance frameworks simultaneously—HIPAA for healthcare clients, PCI-DSS for financial services clients, SOC2 for SaaS clients, CIS Benchmarks for all clients. The platform contextualizes each violation against relevant compliance frameworks and automatically routes violations with appropriate context to the responsible teams.


## Intelligent, Decentralized Approval Routing for Distributed Teams

The critical innovation: instead of routing all remediation requests through a centralized security team, autobotAI automatically identified the appropriate infrastructure team responsible for each violation based on cloud resource metadata, client association, and team ownership information.


## When a violation is detected, autobotAI:

- Enriches the finding with resource ownership metadata, client association, and compliance context from the CMDB
- Automatically identifies the infrastructure team responsible for that client environment or cloud account
- Routes the remediation request directly to the infrastructure team via Slack with client and compliance context
- Infrastructure team reviews and approves (or rejects) the proposed fix in context of their client's needs
- autobotAI auto-remediates once approved, or logs rejection for security team review
- Complete audit trail captures the violation, team, client, compliance context, approval decision, remediation action, and outcome


## Generative AI-Powered Context and Summarization for Distributed Teams

autobotAI leveraged generative AI capabilities to automatically summarize complex security findings, remediation recommendations, and compliance implications. When violations were detected across client environments, the platform used language models to:

●	Summarize technical findings — Converting raw CSPM data into clear summaries for distributed infrastructure teams

●	Generate compliance context — Automatically translating violations into language relevant to the specific client's compliance requirements

●	Clarify client impact — Explaining how violations affect the specific client's workload, data, or compliance posture

●	Provide remediation context — Creating detailed explanations of risks, business impact, and recommended fixes

●	Correlate multi-client patterns — Identifying similar violations across multiple clients and suggesting coordinated remediation approaches

This AI-powered summarization ensured that distributed infrastructure teams could quickly understand violations in their specific client context and make informed approval decisions, without requiring deep security expertise.


## Human-in-Loop Approval Framework

Not all violations could be auto-remediated. autobotAI implemented intelligent approval logic distinguishing routine fixes from critical changes:

Low-Risk Fixes — Automatically remediated with audit logging (e.g., enable logging, rotate unused keys)

Medium-Risk Changes — Routed to infrastructure teams via Slack with AI-generated context and proposed fix

High-Risk Infrastructure Changes — Escalated to security team for review and approval

Client-Critical Changes — Requiring infrastructure team and client stakeholder coordination

Compliance-Critical Actions — Required multi-step approvals with full audit trails and compliance documentation

This intelligent routing prevented alert fatigue while maintaining governance for critical decisions and client trust.


## Serverless, Self-Hosted Multi-Cloud Architecture

autobotAI was deployed as a containerized, self-hosted application within the customer's infrastructure, ensuring:

Data Residency — All security findings, remediation decisions, and audit trails remained within the customer's secure environment

Client Data Protection — Complete segregation and control over client security data aligned with client contracts and compliance requirements

Compliance — Full control over security data aligned with regulatory requirements (GDPR, HIPAA, PCI-DSS, SOC2)

Scalability — Serverless architecture automatically scaled to handle thousands of CSPM findings across 800+ accounts and multiple client environments

Reliability — Multi-region deployment across AWS, Azure, GCP ensuring high availability for client-facing infrastructure


## Implementation Approach

Phased Integration Strategy — autobotAI worked with the customer's infrastructure, security, and client management teams to integrate systems via APIs. The platform augmented existing tools rather than replacing them.

Iterative Workflow Development with Client Context — The team incrementally developed 30+ workflows, starting with high-impact domains (IAM remediation, multi-compliance automation). Each workflow was tested with multiple client scenarios, refined with feedback, and deployed with appropriate approval gates.

Multi-Tenant Compliance Framework — autobotAI was configured to handle multiple client compliance requirements, automatically contextualizing violations and remediation to specific client needs.

Distributed Team Enablement — Infrastructure teams were trained and empowered to make remediation decisions for their client environments, shifting from centralized security review to distributed responsibility.

Scale Optimization — The serverless architecture was designed to handle 800+ accounts, thousands of daily violations across multiple clients, and multi-step approval workflows without performance degradation.


## Dramatically Accelerated Client Infrastructure Deployment

Posture violations that previously sat for days now remediate in hours through intelligent orchestration, decentralized team approvals, and automated execution. Infrastructure teams could make quick decisions about their client environments without waiting for centralized security review, accelerating client infrastructure deployment and time-to-market for client projects.


## Unified Multi-Cloud Posture Management Across Client Environments

For the first time, the organization achieved centralized visibility and control across AWS, Azure, and GCP while maintaining client context and compliance requirements. Security teams view violations, remediation status, and compliance posture across all 800+ accounts and multiple clients in a single platform—with rich client and compliance context.


## Distributed Team Empowerment and Accountability

By routing approvals to responsible infrastructure teams, security responsibility shifted from a centralized model to distributed, collaborative ownership aligned with client assignments. Infrastructure teams became directly accountable for their client environments' security posture, improving security culture and client trust.


## Multi-Client Compliance Management

With 30+ workflows enforcing multiple compliance frameworks simultaneously, the organization achieved compliance consistency across diverse client requirements. Each violation was contextualized to the relevant compliance framework, and remediation was automatically verified against compliance requirements.


## Eliminated Manual Workflow Overhead and Improved Efficiency

The 30+ automated workflows removed repetitive manual tasks—ticket creation, compliance context gathering, stakeholder coordination, manual remediation, verification, and documentation. Infrastructure and security teams were freed from operational toil to focus on strategic security initiatives and client-focused security advisory work.


## Improved Decision-Making Through AI-Powered Contextualization

Generative AI-powered summarization transformed how violations were communicated to distributed infrastructure teams. Complex security findings were automatically translated into clear, client-specific, compliance-relevant language, enabling faster and more informed remediation decisions by teams without deep security expertise.


## Automated Multi-Compliance Audit Readiness

With 30+ workflows enforcing multiple compliance policies across client environments, the organization achieved continuous compliance readiness rather than periodic audit cycles. Audit trails were automatically generated with complete compliance context, dramatically reducing audit preparation effort and demonstrating strong compliance posture to clients.


## Enhanced Distributed Team Collaboration

Slack-integrated notifications, approvals, and AI-generated remediation summaries improved coordination across geographically distributed infrastructure teams. Teams received clear, contextual information about violations specific to their client environments and could make confident decisions within their existing communication platform.


## Complete Operational and Client Visibility

With orchestration through autobotAI and comprehensive audit logging, security leadership gained visibility into remediation timelines, team performance, compliance posture by client, and business impact—enabling data-driven operational decisions and stronger client trust.


## Operational Efficiency at Enterprise Scale Supporting Multiple Clients

The organization sustained operational efficiency managing 800+ accounts across multiple clients with lean infrastructure and security teams. Teams could focus on architecture, strategic security initiatives, client advisory work, and emerging threats rather than operational toil.


## Technical Architecture Summary

autobotAI is deployed as a serverless orchestration platform within the customer's multi-cloud environment. Key architectural elements:

●	Workflow automation engine — Orchestrating 30+ security and compliance workflows across 800+ accounts and multiple client environments

●	Unified integration layer — Connectors to Trend Micro Conformity, AWS, Azure, GCP, Kubernetes, and internal CMDB with client and team context

●	Generative AI context layer — Automatically summarizing violations, generating client and compliance-relevant context, and clarifying business impact in distributed team language

●	Intelligent approval framework — Policy-driven routing to appropriate infrastructure teams with client and compliance context, complete audit trails

●	Serverless architecture — Automatic scaling to handle 800+ accounts, multiple clients, and thousands of daily violations


## Lessons Learned


Distributed team empowerment improves both speed and accountability — Routing approvals to infrastructure teams responsible for client environments enables faster decisions and distributed security accountability.

Multi-client context requires intelligent contextualization — Generic remediation guidance isn't enough for multi-client environments. AI-powered summarization that provides client-specific and compliance-specific context enables better decisions.

Client context is critical metadata — Maintaining accurate, current client associations in resource metadata enables sophisticated client-aware orchestration and compliance tracking.

Workflow automation enables multi-cloud consistency at scale — Managing AWS, Azure, GCP separately creates silos. Unified automation enables consistent security posture across clouds and clients while respecting platform-specific requirements.

Security culture improves with distributed responsibility and empowerment — Moving from centralized security teams to distributed, client-aware infrastructure team responsibility improves security accountability, culture, and client trust.


## Looking Ahead

The customer continues expanding automation coverage:

Advanced Threat Detection Integration Across Client Environments — Building workflows to ingest cloud-native threat detection (AWS GuardDuty, Azure Sentinel, Google Cloud Security Command Center) and orchestrate incident response across multi-cloud environments and multiple clients.

Infrastructure-as-Code Compliance for Client Projects — Extending automation to enforce security policies at the infrastructure-as-code stage, catching violations before client infrastructure is deployed.

Enhanced AI-Powered Client Risk Assessment — Expanding generative AI capabilities to provide predictive risk assessment and strategic security recommendations across client portfolios.

Client Security Dashboard and Reporting — Developing client-facing security posture dashboards and automated compliance reporting to strengthen client trust and demonstrate security value.

Extended GRC and Governance Integration — Integrating with governance, risk, and compliance platforms to correlate cloud security posture with client compliance requirements and enable strategic compliance planning.




Read how this company leveraged AutobotAI Platform

Global IT Services Enterprise: Multi-Cloud Security at Scale


## About

A leading global enterprise managing complex, distributed technology infrastructure across cloud-native applications, microservices, and dynamic development environments. The organization prioritizes proactive security as a critical enabler for business continuity, regulatory compliance, and customer trust.

Operating at significant scale with hundreds of developers and millions of lines of active code, the customer required advanced threat intelligence integration and seamless security automation embedded directly into development workflows.

## The Challenge

As the organization scaled, security and development teams faced mounting operational friction:

## Fragmented Threat Intelligence

The security team maintained multiple risk sources—vulnerability assessment and penetration testing (VA/PT) reports, software composition analysis (SCA) tools, cloud security posture management platforms, and external threat intelligence feeds.
Aggregating and correlating these disparate streams required significant manual effort, often leading to delayed threat detection and incomplete risk analysis.

## Lack of Predictive Threat Modeling

Manual collation of risk data made it nearly impossible to proactively identify emerging attack paths or correlate risks across Cloud, On-premises and vendor SaaS systems. Without predictive analytics, security operations remained fundamentally reactive—detecting issues after exposure, rather than anticipating threats before they materialized.

## Slow AppSec Remediation Cycles

Application security was managed through staged, manual reviews and periodic code assessments. Security findings from pull request (PR) reviews and comprehensive code scans were processed slowly, often requiring days to weeks for remediation. This created extended windows of vulnerability exposure and increased the likelihood of reintroducing previously identified security issues.

## Tool Silos and Manual Orchestration

Security controls, development platforms, and infrastructure management systems operated in silos. Coordinating between vulnerability scanners, code review platforms, issue tracking, and communication tools required manual handoffs. This lack of orchestration led to delays, duplicated effort, and missed correlations between related findings.

## Development Friction and Alert Fatigue

Developers faced constant interruptions from security alerts, many of which were duplicative or low-priority. Manual remediation workflows created friction in development velocity and contributed to alert fatigue across both security and engineering teams.


## The Solution

The customer deployed autobotAI, a unified cyber agent workflow builder platform, to design and operationalize two critical security workflows: predictive threat modeling and automated AppSec remediation. By building intelligent, multi-agent workflows on autobotAI, the organization transformed fragmented tooling into coordinated, autonomous security operations.


## Unified Integration Layer


autobotAI integrated with the customer's core security and development platforms:
VA/PT Tools — ingesting scheduled and ad hoc vulnerability assessment and penetration testing reports

Software Composition Analysis (SCA) — continuous monitoring of open source and third-party dependency risks

Cloud Security Posture Management (CSPM) — identification of cloud misconfigurations and policy violations

External Threat Intelligence — live feeds on emerging threats, indicators of compromise, and adversary tactics applied on existing attack surface from threat intel.
Development Platforms — deep integration with Git, PR systems, CI/CD pipelines, and code review tools

Issue Tracking & Collaboration — Jira, Slack, and internal communication platforms for workflow orchestration and approvals

This created a unified orchestration layer where autobotAI could ingest data, make intelligent decisions, and execute coordinated actions across the entire security and development stack.


## Predictive Threat Modeling Agent Workflow

Using autobotAI's workflow builder, the security team constructed a sophisticated threat modeling agent that:

Normalized and Correlated Risk Data — automatically ingesting VA/PT reports, SCA findings, cloud posture violations, and threat intelligence, then normalizing across disparate formats and correlating related findings

Applied Predictive Analytics — identifying emerging attack paths, risk clusters, and high-impact threat patterns by analyzing historical trends, threat intelligence feeds, and asset relationships

Automated Prioritized Risk Register — continuously updating a ranked, unified risk inventory based on exploitability, business impact, asset criticality, and active threat landscape intelligence

Mapped Threats to Assets — linking identified threats to specific applications, infrastructure components, and development teams for targeted remediation


## Automated AppSec Remediation Agent Workflow

Building on predictive threat modeling outputs, the organization constructed a specialized AppSec remediation agent that:

Integrated with Development Pipelines — connecting directly to Git repositories, PR systems to evaluate security posture in real time

Automated PR and Code Review — analyzing code changes against new and historical vulnerabilities, and security policies during pull request submission
 from developers.

Orchestrated Intelligent Remediation — generating remediated code with detailed code comment, actionable tickets; suggesting fixes based on common patterns and best practices; or directly remediating code issues and agent commit updates to new branch and send new PR details to developer for testing. 

Coordinated Multi-Step Approvals — routing high-risk fixes through approval workflows via Slack and Jira, with full audit trails and enforcement of authorization before deployment

Maintained Feedback Loops — tracking remediation outcomes, measuring fix quality, and continuously improving agent recommendations based on developer feedback and post-deployment validation

This end-to-end automation eliminated manual review bottlenecks, significantly reduced mean time to remediate vulnerabilities, and increased application security resilience at scale.


## Orchestrated Security Operations

autobotAI functioned as the orchestration backbone—coordinating threat modeling and AppSec remediation workflows with the customer's collaboration and task-tracking platforms. Security incidents, high-risk findings, remediation actions, and approvals flowed through automated workflows with complete auditability, delivering transparency and governance while maintaining strict data residency and privacy controls.


## Implementation Approach

Multi-Agent Workflow Development


autobotAI engineers collaborated with the customer's security, development, and infrastructure teams to help them build sophisticated threat modeling and AppSec remediation agents on autobotAI. The implementation followed an iterative, validated approach:


Phase 1: Integration & Data Normalization — Seamless integration with VA/PT, SCA, CSPM, and threat intelligence feeds; normalizing data formats; and establishing baseline correlations without any coding skill or complex automation knowledge.

Phase 2: Predictive Threat Modeling — Threat modeling agent integrated with multiple LLM models to identify attack paths, correlate risks, and generate prioritized risk registers.

Phase 3: AppSec Automation — AppSec remediation agent for PR-stage analysis, automated fix suggestions, and orchestrated remediation workflows

Phase 4: Workflow Refinement — testing in non-production, gathering feedback from developers and security teams, and refining agent behavior based on real-world outcomes


## Policy-Driven Automation

All remediation workflows were configured with granular policies that distinguished routine fixes from critical changes. Low-risk, zero-business-impact remediations were automated end-to-end. Changes affecting production systems, incurring infrastructure costs, or requiring maintenance windows triggered intelligent approval workflows via Slack, with stakeholder routing, response tracking, and enforcement of authorization before execution.

## Serverless, Scalable Deployment

autobotAI was deployed as a fully serverless application within the customer's cloud environment, providing automatic scaling to handle high-volume security event processing, batch threat analysis, and multi-step remediation workflows without infrastructure overhead.


## Technical Architecture Summary

●	Multi-agent AI orchestration for threat modeling, correlations, predictive analytics, and remediation decisions

●	Flexible and Simple integration layer connecting VA/PT, SCA, CSPM, threat intelligence, development platforms, and collaboration tools

●	Policy-driven remediation engine executing automated fixes at code and infrastructure layers, with approval gates for high-risk actions

●	Workflow builder enabling security teams to design custom threat modeling and remediation agent behaviors

●	Scalable, serverless architecture within the customer's secure cloud environment with automatic scaling and minimal operational overhead

●	Comprehensive audit and compliance framework maintaining full action trails, approval records, and outcome tracking for regulatory and governance requirements

●	Bidirectional integrations for ingestion (threat data, code changes) and execution (code fixes, ticket creation, notifications)


## Accelerated Application Security

By automating remediation at the PR stage, the organization:

●	Eliminated manual code review bottlenecks and reduced time from finding to fix

●	Improved developer experience by providing automated remediation suggestions within their existing workflows

●	Reduced recurring vulnerabilities through automated pattern detection and consistent policy enforcement

●	Increased overall application resilience through continuous, coordinated security validation


## Operational Efficiency Gains

●	Reduced analyst workload by automating threat data correlation, prioritization, and initial triage

●	Eliminated manual ticket creation and coordination through end-to-end workflow automation

●	Freed security teams for higher-value activities like threat hunting and architecture reviews, rather than repetitive data aggregation and remediation tracking

●	Improved MTTR tracking and SLA compliance with automated audit trails and transparent remediation metrics


## Enhanced Team Collaboration

With autobotAI's multi-agent orchestration:

●	Security, development, and infrastructure teams collaborated around unified risk intelligence.

●	Developers received actionable security guidance within their existing workflows.

●	Approvers gained full context and audit trails for critical remediation decisions.

●	Real-time Slack notifications and Jira integration improved coordination and reduced context-switching


## Continuous Compliance & Governance

●	Complete auditability across threat detection, correlations, remediation actions, and approvals

●	Policy-aligned automation ensuring remediation adhered to organizational standards and compliance requirements

●	Transparent risk tracking enabling security leadership to measure progress and demonstrate compliance improvements to stakeholders


## Looking Ahead

The customer is expanding the autobotAI platform with new security agent workflows and enhanced  multi-agent workflow capabilities for use cases like DLP and IAM operations automation.

Fortune 500 Enterprise: From Reactive to Predictive Security Operations


## About Grene Robotics


**Grene Robotics** is a Hyderabad-based enterprise software company focused on workplace automation and hyperautomation. Founded in 2008, the company builds **GreneOS**, a unified AI-powered platform that connects people, processes, data, and systems to deliver autonomous business operations.

The platform leverages artificial intelligence, machine learning, and RPA (Robotic Process Automation) to automate enterprise workflows across organisations. Grene Robotics serves large enterprises in telecommunications, technology, and professional services.

Their cloud-native infrastructure and Kubernetes workloads require robust security operations and continuous compliance monitoring to protect customer data and maintain service reliability.


## Continuous Compliance Management Complexity


With cloud infrastructure on AWS and containerized workloads on Kubernetes, Grene Robotics needed to maintain continuous compliance across multiple environments. Governance, Risk, and Compliance (**GRC**) violations were detected by various security scanners, but turning findings into timely remediation required manual effort.

Security teams spent time reviewing violations, defining remediation steps, coordinating with IT operations, and tracking resolution across cloud and container environments.


## Manual Security Incident Response


Security findings from multiple tools — including Trend Micro, AWS WAF, and AWS Inspector — generated alerts that required manual investigation. Analysts had to correlate findings, assess severity, decide actions, and coordinate remediation with development and operations teams. This manual workflow created delays in addressing threats.


## Multi-Environment Threat Surface


Protecting more than **20** threat vectors across cloud infrastructure and containerised applications created a complex security landscape. Container-specific threats, cloud misconfigurations, and traditional vulnerabilities required different remediation approaches, and manual management strained security and IT operations.

## Tool Silos and Workflow Coordination


Security operations required coordination across multiple tools and teams, with Google Chat used for collaboration. Findings from AWS, Trend Micro, and other tools had to be routed to the right teams, remediation coordinated, and approvals obtained for critical changes — all via manual processes that slowed response times.

## Operational Burden on Security and IT Teams


The combination of continuous compliance monitoring, incident response, and threat remediation across cloud and Kubernetes environments created a high operational burden. Security and IT teams spent extensive time on repetitive manual work instead of strategic security improvements and proactive threat hunting.

## The Solution


Grene Robotics deployed **autobotAI** as their security operations automation platform, integrating it with the existing stack to automate compliance remediation, incident response, and threat mitigation across cloud and container environments.


## Unified Security Integration Layer


autobotAI integrated with Grene Robotics' security and infrastructure tools:

- **Trend Micro** and **Snyk** for cloud security findings
- **AWS** native services including **AWS WAF** and **AWS Inspector** for cloud security monitoring
- **Kubernetes** for container workload security and compliance
- **Google Chat** for team collaboration, notifications, and approvals
- **GRC** tooling and asset inventory systems for compliance violation detection

This created a unified orchestration layer where autobotAI could ingest findings from multiple sources, make remediation decisions, and execute coordinated actions across cloud and container environments.


## 20+ Automated Security and Compliance Workflows


Working with Grene Robotics' security and IT teams, autobotAI engineers built over **20** automated workflows covering compliance remediation, incident response, and threat mitigation.


## Continuous Compliance Automation


autobotAI continuously monitors for GRC violations across cloud and Kubernetes workloads. When violations are detected, the platform assesses the finding, determines remediation steps, and executes fixes based on policy.

Low-risk, zero-cost changes are remediated automatically. Critical infrastructure changes use smart approval workflows via Google Chat — routing requests to stakeholders, tracking approvals, and executing changes only after authorization.


## Automated Incident Response


Security findings from Trend Micro, AWS WAF, AWS Inspector, and other services are ingested and correlated by autobotAI. The platform assesses severity, collects context about affected resources, determines remediation steps, and executes response playbooks.

Critical events trigger immediate Google Chat notifications with full context; lower-priority findings are handled automatically with complete audit trails.


## Cloud and Container Security Orchestration


autobotAI orchestrates remediation across AWS and Kubernetes environments. The platform handles cloud misconfigurations, container security findings, network policy violations, and access control issues through unified workflows that understand the requirements of each environment.

Protection spans more than **50** threat vectors including container-specific threats, cloud vulnerabilities, and attack surface exposures.


## Smart Approval Framework


All workflows include intelligent approval logic that distinguishes routine remediation from critical changes. Tasks with no cost impact and no maintenance windows are automated end-to-end. Changes that could affect production, incur cost, or require maintenance trigger smart approval workflows via Google Chat.

The system routes approval requests to stakeholders based on change type and risk, tracks responses, maintains audit trails, and executes approved changes automatically.


# Implementation Approach


# Implementation approach

## Serverless deployment

autobotAI was deployed as a fully serverless application in AWS environment. This deployment provides automatic scaling, eliminates infrastructure management overhead, and aligns with Grene Robotics' cloud-native approach.

## Workflow development partnership

autobotAI engineers collaborated with Grene Robotics' security and IT teams to build **20+** workflows over the implementation period. Starting with high-impact use cases such as compliance violations and incident response, the teams expanded automation across security operations. Each workflow was tested in non-production environments, refined with feedback, and deployed with appropriate approval gates.

## Multi-environment integration

The platform was configured to handle both cloud and container environments. autobotAI integrates with Kubernetes APIs for container security management while connecting to AWS services for cloud protection—enabling unified security operations across the stack.

## Collaboration via Google Chat

Integration with Google Chat provided a seamless collaboration experience. Security teams receive notifications, review findings, approve critical changes, and track remediation status within their existing communication platform—reducing context switching and accelerating decision-making.

# Results

## 55% reduction in manual security and IT operations

The deployment of autobotAI and development of **20+** automated workflows delivered measurable operational improvements.

## Automated security and IT operations

Grene Robotics achieved a **55%** reduction in manual operations across security and IT teams. Tasks that previously required hands-on intervention—reviewing compliance violations, investigating findings, coordinating remediation, and tracking resolution—are now handled automatically by intelligent workflows. This freed teams to focus on architecture improvements, threat hunting, and platform innovation.

## Continuous compliance achieved

Rather than periodic compliance reviews with backlogs, Grene Robotics now maintains continuous compliance across cloud and Kubernetes environments. GRC violations are identified and remediated automatically, improving posture and simplifying audits.

## Comprehensive threat protection

autobotAI provides automated protection across more than **50** threat vectors spanning cloud and containerised applications. Container threats, cloud misconfigurations, network vulnerabilities, and access control issues are addressed through automated remediation workflows.

## Faster incident response

Incidents that previously required manual investigation and coordination now resolve via automated playbooks. Integration with Trend Micro, AWS WAF, and AWS Inspector enables immediate automated response to findings, reducing exposure windows.

## Smart approval workflow efficiency

The intelligent approval framework balances automation with oversight. Routine, low-risk remediation is automated, while critical changes route through Google Chat approvals with full audit trails.

## Unified cloud and container security

Grene Robotics now operates unified security across AWS and Kubernetes environments. autobotAI provides consistent automation and remediation workflows that understand environment-specific requirements.

## Enhanced team collaboration

Google Chat integration streamlined communication and coordination. Notifications, approval requests, remediation updates, and incident alerts flow through the team's existing platform—improving visibility and accelerating response.

## Looking ahead

Grene Robotics continues to expand automation across security domains and integrate additional tools. The success shows that security operations automation—connecting visibility, DevOps, and action platforms like AWS and Jira—enables teams to operate at scale.


Grene Robotics


## About Razorpay

**Razorpay** is India’s leading payment gateway and fintech platform, enabling businesses to accept, process, and disburse payments across multiple channels. The company processes millions of transactions daily, serving businesses across India and internationally.

With sensitive financial data and high-volume operations, Razorpay’s security posture spans incident response, compliance remediation, vulnerability management, and access control across a distributed cloud environment.


## The Challenge

As Razorpay scaled, their security operations team faced challenges in operationalizing security across multiple domains. Key pain points included:

## Incident response bottlenecks

The Security Operations Center (SOC) received thousands of alerts daily from SIEM, threat-intel feeds, and cloud security tools. Manual triage and investigation created significant delays between detection and containment, increasing exposure for critical payment infrastructure.

## Compliance remediation gaps

Although Razorpay used Cloud Security Posture Management (CSPM) tools to flag violations and misconfigurations, converting findings into action was manual and slow. Teams reviewed CSPM reports, assessed violations, created remediation tickets, coordinated with engineering, and tracked resolution — often leaving critical issues open for extended periods.

## User access management complexity

Onboarding and offboarding required manual coordination across systems. Provisioning and revocation of access involved multiple teams and tools, which led to delays and occasional oversights.

## Attack-surface remediation delays

Vulnerability scans and asset discovery surfaced exposed attack surfaces, but coordinating remediation across distributed teams was time-consuming. Creating tickets, assigning ownership, and tracking progress required constant manual follow-up.

## Tool silos and manual orchestration

Razorpay’s security stack included AWS, SIEM, threat-intel platforms, ticketing systems, and collaboration tools that largely operated in silos. Analysts manually stitched together workflows—enriching SIEM alerts, creating Jira tickets, coordinating via Slack, and duplicating documentation—leading to inefficiencies and inconsistent processes.


## The Solution

Razorpay deployed **autobotAI** as a security orchestration and automation platform to integrate with the existing stack and automate workflows across incident response, compliance remediation, and operational security.

### Unified integration layer

autobotAI integrated with Razorpay’s core tools:

- **AWS** — cloud infrastructure visibility and control
- **SIEM** — alert ingestion and correlation
- **MISP** — threat intelligence enrichment
- **Jira** — automated ticket creation and tracking
- **Slack** — notifications, approvals, and collaboration
- **CSPM tools** — findings ingestion (autobotAI handles remediation)

This created a unified orchestration layer that ingests data, makes decisions, and executes coordinated actions—reducing manual workflows.

## 30+ automated security workflows

Razorpay’s team built over **30** automated workflows using autobotAI, covering key areas:

## Incident response automation

SIEM alerts are correlated and enriched with MISP threat intelligence. The platform assesses severity, gathers context from AWS and other sources, and executes response playbooks. Critical threats trigger immediate Slack notifications while lower-priority events are managed via Jira tickets.

## Compliance remediation orchestration

When CSPM tools flag violations, autobotAI orchestrates remediation: executing approved fixes in AWS, creating detailed Jira tickets for changes needing review, routing approvals via Slack, and updating tickets with results.

## User lifecycle automation

Onboarding/offboarding tickets in Jira drive automated provisioning or revocation across systems. autobotAI ensures role-appropriate access and updates Jira with completion and audit details.

## Attack-surface remediation workflows

Detected exposures generate Jira tickets with MISP context, assigned to owners, tracked against SLAs, and escalated via Slack when necessary.

## Cross-system orchestration

Complex workflows span multiple systems (for example: CSPM finding → MISP lookup → AWS check → Jira ticket → Slack approval → AWS remediation → SIEM log → Jira update). autobotAI automates these multi-step sequences.

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## Implementation approach

> **Integration strategy** - autobotAI worked with Razorpay’s security, IT, and engineering teams to integrate systems via APIs and webhooks—augmenting the existing stack rather than replacing it.

> **Workflow development** - Razorpay incrementally developed **30+** workflows, starting with high-impact cases (incident response and compliance). Each workflow was tested in non-prod, refined with feedback, and deployed with approval gates.

> **Multi-step approval framework** - For sensitive changes, autobotAI implements configurable multi-step approvals via Slack, with complete audit trails and enforced execution only after authorization.

> **Scale optimization** - Designed for Razorpay’s scale, the serverless architecture handles high volumes of security events and batch processing without degradation.

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## 73% reduction in mean time to respond (MTTR)

Deployment of autobotAI and 30+ automated workflows delivered measurable improvements across security operations.

## Faster incident response

A **73%** reduction in MTTR was the most significant outcome. Incidents that once took hours now resolve in minutes through automated playbooks and integrated tooling.

## Accelerated compliance remediation

CSPM-identified violations are remediated within hours instead of days. autobotAI bridges visibility and action by automating remediation and coordinating approvals where necessary.

## Streamlined user lifecycle

Automated provisioning and deprovisioning via Jira eliminated delays and reduced the risk of orphaned accounts.

## Improved attack-surface management

Automated ticketing, assignment, tracking, and escalation ensure exposures are addressed within SLAs with clear ownership.

## Eliminated manual integration work

The 30+ automated workflows removed repetitive manual tasks—freeing analysts for higher-value activities like threat hunting.

## Enhanced team collaboration

Slack-driven notifications and approvals improved coordination across security, IT, and engineering while preserving oversight for critical actions.

## Complete operational visibility

With orchestration through autobotAI and tracking in Jira, security leadership gained visibility into response times, remediation status, approvals, and team workloads—enabling data-driven decisions.

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## Looking ahead

Razorpay continues to expand automation across security domains and integrate additional tools. The success demonstrates that security operations automation—connecting visibility, DevOps, and action platforms like AWS and Jira—enables teams to operate at scale.

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## Technical architecture summary

autobotAI is deployed as a serverless orchestration platform within Razorpay’s cloud environment. Key elements:

- Multi-agent AI for decision-making, correlation, and orchestration
- Integration layer connecting AWS, SIEM, MISP, Jira, and Slack
- Workflow engine coordinating 30+ automated workflows
- Multi-step approval framework with audit trails
- Serverless architecture for automatic scaling
- Bidirectional integrations for ingestion (SIEM/CSPM) and execution (AWS/Jira/Slack)
- All orchestration occurs within Razorpay’s environment—sensitive data remains inside their infrastructure


Case Studies

Global IT Services Enterprise: Multi-Cloud Security at Scale

Fortune 500 Enterprise: From Reactive to Predictive Security Operations

Grene Robotics

RazorPay

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