AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
Replacing manual alert triage and reactive remediation with an autonomous multi-agent security enforcement layer. Phase 1 is live across 12 of 18 AWS accounts — already remediating S3 and security group drift in under 5 minutes, with full IAM and compliance coverage rolling out by end of 2026.
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
Results So Far
Results So Far
0%+
REMEDIATION SPEED
2–6 hrs → < 5 min
~0%
DRIFT EXPOSURE
2–4 days → < 1 hr
0%
AUTO-FIX RATE
S3 + security groups
0%
ANALYST WORKLOAD
reduction so far
About The Customer
About The Customer
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
The Problem
The Problem
80–150 drift events/week
Configuration drift across IAM policies, S3 permissions, security groups, and NACLs — driven by fast-moving releases and IaC inconsistencies
~30% unresolved backlog
3-person security team could not triage everything. Exposure windows sitting open for 2–4 days
2–6 hour remediation time
Manual ticket creation, context gathering, Slack conversations with DevOps, and senior analyst approval
600+ alerts/week
Security Hub and GuardDuty alert fatigue. Roughly half of high-severity findings not actioned within SLA
8–12 audit findings/quarter
Remediation sprints pulling engineers off product work. $300K–$500K annually in labor, audit fees, and delayed releases
The Solution
The Solution
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Live
Drift Detection Agent
Ingests real-time AWS Config and CloudTrail events, identifies deviations from security baselines across S3, security groups, and NACLs. Currently covering 12 of 18 accounts.
Live
Contextual Risk Reasoning Agent
Conducts autonomous outbound calls in Hindi and English with clinical context awareness. Explains result urgency in simple terms and guides patients through booking.
Live
Auto-Remediation Agent
Executes policy-driven fixes for low/medium risk drift autonomously — revoking public S3 access, closing open ports. S3 and security group drift; IAM in Phase 2
Phase 2
Escalation & HIL Agent
Routes complex cases to human coordinators with full context and recommended actions. Supervisory dashboard for override and audit.
Phase 2
Compliance Posture Agent
Continuous compliance state across SOC 2 and ISO 27001, generating audit-ready evidence automatically. Replaces quarterly snapshots.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)
AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
Replacing manual alert triage and reactive remediation with an autonomous multi-agent security enforcement layer. Phase 1 is live across 12 of 18 AWS accounts — already remediating S3 and security group drift in under 5 minutes, with full IAM and compliance coverage rolling out by end of 2026.
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
Results So Far
Results So Far
0%+
REMEDIATION SPEED
2–6 hrs → < 5 min
~0%
DRIFT EXPOSURE
2–4 days → < 1 hr
0%
AUTO-FIX RATE
S3 + security groups
0%
ANALYST WORKLOAD
reduction so far
About The Customer
About The Customer
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
The Problem
The Problem
80–150 drift events/week
Configuration drift across IAM policies, S3 permissions, security groups, and NACLs — driven by fast-moving releases and IaC inconsistencies
~30% unresolved backlog
3-person security team could not triage everything. Exposure windows sitting open for 2–4 days
2–6 hour remediation time
Manual ticket creation, context gathering, Slack conversations with DevOps, and senior analyst approval
600+ alerts/week
Security Hub and GuardDuty alert fatigue. Roughly half of high-severity findings not actioned within SLA
8–12 audit findings/quarter
Remediation sprints pulling engineers off product work. $300K–$500K annually in labor, audit fees, and delayed releases
The Solution
The Solution
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Live
Drift Detection Agent
Ingests real-time AWS Config and CloudTrail events, identifies deviations from security baselines across S3, security groups, and NACLs. Currently covering 12 of 18 accounts.
Live
Contextual Risk Reasoning Agent
Conducts autonomous outbound calls in Hindi and English with clinical context awareness. Explains result urgency in simple terms and guides patients through booking.
Live
Auto-Remediation Agent
Executes policy-driven fixes for low/medium risk drift autonomously — revoking public S3 access, closing open ports. S3 and security group drift; IAM in Phase 2
Phase 2
Escalation & HIL Agent
Routes complex cases to human coordinators with full context and recommended actions. Supervisory dashboard for override and audit.
Phase 2
Compliance Posture Agent
Continuous compliance state across SOC 2 and ISO 27001, generating audit-ready evidence automatically. Replaces quarterly snapshots.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)
AIspresso Labs
AIspresso Labs
AIspresso Labs
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
How a Mid-Size Financial Services Firm Is Cutting Cloud Remediation Time from Hours to Seconds with Agentic AI
Replacing manual alert triage and reactive remediation with an autonomous multi-agent security enforcement layer. Phase 1 is live across 12 of 18 AWS accounts — already remediating S3 and security group drift in under 5 minutes, with full IAM and compliance coverage rolling out by end of 2026.
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
PROJECT STATUS: Phase 1 Complete | Phase 2 In Progress — Target: End of 2026
Results So Far
Results So Far
0%+
REMEDIATION SPEED
2–6 hrs → < 5 min
~0%
DRIFT EXPOSURE
2–4 days → < 1 hr
0%
AUTO-FIX RATE
S3 + security groups
0%
ANALYST WORKLOAD
reduction so far
About The Customer
About The Customer
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
A mid-size financial services and insurance firm with approximately $800M in annual revenue, headquartered in the U.S. with operations across North America and Europe. Manages 18 AWS accounts, ~3,500 IAM roles, ~800 S3 buckets. A lean DevOps team of ~25 engineers ships 60–80 releases per week across three business units — with only a 3-person security team to keep pace.
The Problem
The Problem
80–150 drift events/week
Configuration drift across IAM policies, S3 permissions, security groups, and NACLs — driven by fast-moving releases and IaC inconsistencies
~30% unresolved backlog
3-person security team could not triage everything. Exposure windows sitting open for 2–4 days
2–6 hour remediation time
Manual ticket creation, context gathering, Slack conversations with DevOps, and senior analyst approval
600+ alerts/week
Security Hub and GuardDuty alert fatigue. Roughly half of high-severity findings not actioned within SLA
8–12 audit findings/quarter
Remediation sprints pulling engineers off product work. $300K–$500K annually in labor, audit fees, and delayed releases
The Solution
The Solution
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Most AI initiatives stall because they start with models instead of foundations. They produce one-off pipelines that never integrate into real operational workflows and lose ownership after "go-live."
An Agentic AI-driven Cloud Security Enforcement Platform on AWS Bedrock — an autonomous remediation layer that continuously detects, reasons about, and remediates configuration drift, working alongside the existing 3-person security team.
Live
Drift Detection Agent
Ingests real-time AWS Config and CloudTrail events, identifies deviations from security baselines across S3, security groups, and NACLs. Currently covering 12 of 18 accounts.
Live
Contextual Risk Reasoning Agent
Conducts autonomous outbound calls in Hindi and English with clinical context awareness. Explains result urgency in simple terms and guides patients through booking.
Live
Auto-Remediation Agent
Executes policy-driven fixes for low/medium risk drift autonomously — revoking public S3 access, closing open ports. S3 and security group drift; IAM in Phase 2
Phase 2
Escalation & HIL Agent
Routes complex cases to human coordinators with full context and recommended actions. Supervisory dashboard for override and audit.
Phase 2
Compliance Posture Agent
Continuous compliance state across SOC 2 and ISO 27001, generating audit-ready evidence automatically. Replaces quarterly snapshots.
Outcomes
Outcomes
Metrics
Metrics
Before
Before
After (Agentic AI)
After (Agentic AI)
Impact
Impact
Resolution cycle
Resolution cycle
10–14 days
10–14 days
< 24 hours
< 24 hours
< 24 hours
90% faster resolution
90% faster resolution
90% faster resolution
Error / inconsistency rate
Error / inconsistency rate
15%+
15%+
< 1%
< 1%
93% fewer errors
93% fewer errors
93% fewer errors
Fraud detection
Fraud detection
~3% caught
~3% caught
~12% caught
~12% caught
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
4x more fraud (~$300K+ saved/year)
Customer experience
Customer experience
No visibility
No visibility
Real-time self-service
Real-time self-service
60%+ reduction in calls
60%+ reduction in calls
60%+ reduction in calls
Product quality feedback loop
Defect assessment agent surfaced batch-level failure patterns feeding directly into product engineering, enabling proactive action on two product lines.
Retail partner alignment
Standardized claim processing across retail channels reduced disputes and chargebacks by 40%.
AI Accelerator
AI Accelerator
Pre-configured agent templates for warranty and returns processing, retail partner integration adapters, fraud detection model baselines, and computer vision defect assessment models. Estimated 6–8 weeks from kickoff to production.
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
AI Built For Production
Find us:
San Diego, Bengaluru, London, Dubai
Reach out to us on:
Rewriting critical bottlenecks for scale and performance.
Refactor (For Constraints)