SECURITY ENGINEERING

What We Do

Karhu Cyber’s Security Engineers are certified professionals who solve problems. Applying zero trust principles across cloud platforms, identity, networks, and firewalls, we design, implement, and harden the controls that keep your organization secure, building AI-aware guardrails into everything we do. Engaging our engineers on an as-needed basis is far more cost-effective than hiring full-time, giving you senior expertise and the flexibility to scale effort up or down. Day to day, that hands-on work includes:

  • Zero trust architecture design and implementation
  • Microsoft 365 and Google Workspace hardening
  • Cloud hardening and secure configuration
  • Network segmentation
  • Multi-factor authentication (MFA) rollout
  • Firewall hardening
  • Endpoint hardening and device security
  • Patch and vulnerability remediation
  • Logging, monitoring, and alerting setup
  • Identity and access management (IAM)
  • AI-aware security controls and guardrails

Security Assessments

Two focused engagements to measure where you stand today and where you need to go next — from foundational security gaps to AI-era readiness.

Security Gap Assessment

Not sure where to start? A Security Gap Assessment gives you an objective, unbiased review of your current security posture. We pinpoint weaknesses across your environment before they impact your bottom line and hand you a clear, prioritized plan to close them.

What you get:

  • A full review of your current security posture
  • Prioritized findings ranked by risk
  • A practical remediation roadmap
  • Objective, vendor-neutral guidance
AI Threat Readiness Assessment

AI is reshaping the threat landscape on both sides of the firewall — powering faster attacks while your own teams adopt AI tools that can introduce new risk. This assessment evaluates how prepared you are for that shift and where your AI adoption may be exposing sensitive data.

What we evaluate:

  • Exposure to AI-driven attacks and phishing
  • Security of internal AI and LLM tool use
  • Data privacy and leakage risks in AI workflows
  • Governance, policy, and access controls for AI