Security & Compliance

How we protect your data, infrastructure, and intellectual property

Our Commitment

Security is Built In, Not Bolted On

We handle sensitive client data, proprietary business logic, and production infrastructure. Security isn't a feature we add later. It's embedded in how we build, deploy, and operate every solution.

Every project follows our security baseline: encrypted communications, access-controlled environments, and auditable processes. We adapt our security posture to match your compliance requirements, whether that's SOC 2, GDPR, HIPAA, or industry-specific standards.

Data Encryption

AES-256 at rest, TLS 1.3 in transit

Access Control

Role-based, least-privilege access

GDPR Ready

Data minimization, right to deletion

Audit Trail

Full traceability on every action

Practices

How We Protect Your Data

Our security practices cover the full lifecycle: development, deployment, operations, and data handling.

Infrastructure Security

All client solutions are deployed on enterprise-grade cloud infrastructure (AWS, GCP, or Azure) with:

  • Isolated environments per client (no shared tenancy)
  • VPC/VNet configuration with private subnets
  • Encrypted storage volumes (AES-256)
  • TLS 1.3 for all data in transit
  • Regular security patching and updates

Access Control

We enforce strict access policies across all systems:

  • Role-based access control (RBAC) with least-privilege principle
  • Multi-factor authentication (MFA) on all accounts
  • SSH key-based access to production systems (no passwords)
  • Access reviews and revocation within 24 hours of role changes
  • No client data on personal devices

Application Security

Security is part of our development process, not a post-build checklist:

  • Code reviews on every pull request
  • Dependency scanning for known vulnerabilities
  • Secrets management (no hardcoded credentials)
  • Input validation and output sanitization
  • Rate limiting and abuse protection on all APIs

Data Handling

Your data is treated with care at every stage:

  • Data classification (public, internal, confidential, restricted)
  • Minimal data retention: we only keep what's needed, for as long as needed
  • Secure deletion upon project completion or client request
  • No client data used for training AI models unless explicitly authorized
  • Data residency options available (deploy in your region)

Team Security

Our team follows security-first practices:

  • Background checks on all team members handling client data
  • NDA and confidentiality agreements with every team member
  • Security awareness training on onboarding and quarterly
  • Incident response procedures with defined escalation paths
  • Clean-desk policy for remote workers handling sensitive data

Incident Response

We have a documented incident response process:

  • Detection and triage within 1 hour of discovery
  • Client notification within 24 hours for any data-related incident
  • Root cause analysis and remediation report
  • Post-incident review to prevent recurrence
  • Documented communication chain for escalation
AI Security

AI-Specific Security Considerations

AI agents introduce unique security challenges. Here's how we address them.

LLM API Security

  • API keys stored in secure vaults (AWS Secrets Manager, HashiCorp Vault)
  • All LLM calls routed through our managed proxy for logging and rate control
  • No client PII sent to LLM APIs without explicit consent and data masking
  • Model provider data usage policies reviewed and documented per project

RAG & Data Pipeline Security

  • Vector databases deployed in client-controlled infrastructure when required
  • Document ingestion pipelines with access control and content filtering
  • Retrieval results scoped to user permissions (no unauthorized data leakage)
  • Regular audits of indexed content for accuracy and access compliance

Agent Guardrails

  • Defined action boundaries: agents can only access authorized tools and APIs
  • Human approval gates for high-impact actions (financial transactions, data deletion)
  • Output filtering to prevent sensitive data leakage in agent responses
  • Prompt injection protection through input sanitization and system prompt hardening

Monitoring & Observability

  • End-to-end tracing on every agent execution
  • Anomaly detection for unusual agent behavior patterns
  • Cost monitoring with alerts for unexpected API usage spikes
  • Audit logs retained for compliance review periods
Data Lifecycle

How Your Data Flows Through Our Systems

Transparency about data handling builds trust. Here's exactly what happens with your data during an engagement.

1

Intake & Classification

Data is received through encrypted channels. We classify it by sensitivity level and apply appropriate handling controls.

2

Processing & Storage

Data is processed in isolated environments. Storage is encrypted at rest. Access is limited to team members who need it for the project.

3

Agent Integration

When data feeds into AI agents, PII is masked or anonymized where possible. LLM API calls are logged for auditability.

4

Delivery & Handoff

Deliverables are transferred through secure channels. Client gets full ownership of code and data upon project completion.

5

Retention & Deletion

Client data is securely deleted within 30 days of project completion unless a longer retention is agreed upon in writing.

Questions?

Need More Details on Our Security Practices?

We're happy to discuss specific compliance requirements, share our security documentation, or complete your vendor security questionnaire.

Contact Us