
Fintech Security and Compliance Audit
18
critical/high findings identified and remediated
-40%
audit turnaround vs. fully manual review
0
critical findings in the following regulatory review
Prioritized
remediation ordered by finding severity
A growing fintech company was approaching a regulatory review without enough in-house security capacity for a full audit. Rapid growth had left it without a current, unified view of security gaps across its systems.
- Integrations, environments, and access grants had accumulated under deadline pressure.
- The in-house team did not have enough capacity to conduct the full security review.
- Access, configuration, and monitoring gaps were difficult to assess consistently.
- Unaddressed findings could force remediation under a regulator's deadline.
Manual review of access logs, configurations, and monitoring is slow and inconsistent at scale. AI can help experts analyze more security signals without replacing their judgment.
- Process larger volumes of access logs and configuration data consistently.
- Identify anomalies and configuration drift against known security practices.
- Surface candidate findings for expert validation and prioritization.
- Reduce manual review fatigue while keeping decisions with the security team.
We combined automated analysis of access logs, encryption settings, and monitoring configurations with expert review. The audit produced prioritized findings and concrete remediation steps.
- Automated analysis flagged anomalies and configuration drift.
- Security experts validated findings, removed false positives, and assessed business impact.
- Findings were prioritized as critical, high, medium, or low.
- Concrete remediation steps helped the engineering team work through issues in order.
Python security automation
Security information and event management (SIEM)
Identity and access management (IAM)
Cloud security posture management (CSPM)
Encryption and key management
Configuration drift detection
Security compliance auditing
AI-assisted security log analysis
AI-driven compliance anomaly detection













