Snyft - Cloud-Native SIEM Scaling & AI Auto-Triage

Through close cooperation with Stormit, SNYFT was able to successfully enter the AWS Activate program. Stormit’s guidance helped the startup navigate the application process smoothly, allowing them to secure a significant amount of AWS credits to directly fund their high-volume log ingestion and cloud-native scaling.

ClientSnyft

PublishedMay 2026

IndustryCybersecurity

Websitesnyft.cz

SNYFT is an innovative, cloud-native Security Information and Event Management (SIEM) platform engineered specifically to address the complex compliance and cost challenges faced by SMBs and mid-market organizations.

SNYFT is a cloud-native Security Information and Event Management (SIEM) platform designed specifically for small-to-medium businesses (SMBs) and mid-market companies. Operating on a B2B SaaS model, the platform provides comprehensive security monitoring without the traditional engineering overhead or complexity typical of enterprise-grade solutions.

The company is preparing to launch its pilot phase in early 2026, positioning itself to serve a rapidly growing market driven by strict regulatory demands—such as the EU's NIS2 directive, which mandates incident monitoring for over 100,000 European organizations.

The Market Gap & Technical Symptoms Traditional SIEM platforms are notoriously engineering-heavy, complex to deploy, and cost between $50K to $200K annually. Because of these barriers, lean security teams face severe operational bottlenecks:

Alert Fatigue: Security analysts spend 60–70% of their time performing manual first-pass reviews on incoming alerts.
High Noise Volumes: Rule-based SIEM systems generate a 40–60% false-positive rate, burying genuine cyber threats in thousands of daily alerts.
Delayed Responsiveness: Legacy setups struggle with fragmented tooling and slow log correlation, stretching the Mean Time to Detect (MTTD) to days or weeks.

Business and Compliance Risks Failing to solve these issues leaves companies highly vulnerable to missed data breaches, rapid analyst burnout, and severe regulatory consequences. Under frameworks like NIS2, non-compliance carries penalties of up to €10 million or 2% of global revenue, making affordable threat monitoring a critical legal necessity.

Snyft partnered with Stormit to validate its cloud design via an AWS Well-Architected Framework review. Snyft successfully completed this review process , and both teams continue to actively collaborate on optimizing the architecture to ensure it remains highly scalable, cost-efficient, and prepared for future enterprise-grade growth.

The platform architecture is built natively on AWS, isolating individual customer clusters while integrating advanced AI functionalities:

1. Unified Log Ingestion & Microservices The platform utilizes a five-stage architecture: Ingestion -> Detection -> Triage -> Investigation -> Response. Customer data sources (such as AWS CloudTrail, Microsoft 365, and Google Workspace) feed telemetry via active Celery polling or event-driven Amazon SQS queues. Containerized workers running on Amazon ECS Fargate normalize these entries into a Unified Data Model (UDM).

2. Multi-Tenant Storage & Intelligent Tiering To ensure rigorous isolation, the platform enforces Row-Level Security (RLS) in Amazon RDS for PostgreSQL alongside pg_partman for time-series log partitioning. To prevent high storage costs, SNYFT implements a hybrid analytics tier:

Hot Storage: Recent security logs (<7 days) remain in RDS for sub-millisecond querying.
Cold Storage: Older data is continuously tiered into Amazon S3 as Snappy-compressed Parquet archives with Object Lock enabled for WORM compliance.
Federated Search: Amazon Athena handles ad-hoc historical queries, running asynchronously with hot-path lookups to deliver transparent unified results.

3. AI Automation Engine Amazon Bedrock (utilizing Anthropic Claude models) serves as the core intelligence framework, operating strictly within EU geographic inference profiles to preserve strict European data residency:

Auto-Triage: Bedrock automatically assesses and scores every new alert to filter out noise prior to human contact.
Investigation Assistance: Generates contextual narratives, behavioral timelines, and drives an agentic natural language chat for accelerated incident response.

Automated Incident Triage: By implementing Bedrock's auto-triage and confidence scoring, the platform pre-assesses over 80% of incoming security alerts before an analyst ever opens them.
Instant Investigations: Contextual threat summaries and entity baseline lookups collapse standard log investigations from 30–60 minutes down to immediate, on-screen availability.
Sub-minute Detection Latency: Ingested items are actively correlated against built-in rules, including over 900 community Sigma techniques, providing sub-minute response speeds.
Infrastructure Cost Savings: Deploying containerized workloads on AWS Graviton (ARM64) processors allowed the team to realize immediate compute savings without incurring any performance degradation.

AWS SERVICES

Amazon Bedrock

Amazon RDS

Amazon ECS

Amazon S3

Amazon Athena

Key Takeaways

80%+

Alert Pre-assessment

Sub-minute

Detection Latency

<7 Days

Hot Storage

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