Agentic AI

Cyberforks builds and integrates agentic AI systems — built with a security lens — for organizations that need more than a chatbot demo. Each engagement starts with a real workflow, a real threat model, and a written guardrail spec, delivered by a practitioner who has run a multi-instance agentic system in production every day for months, not someone reading vendor white papers.

AI Assistant Implementation

Building persistent, context-aware AI assistants tailored to your business — not a generic chatbot deploy.

  • Multi-instance agent architecture with per-project context isolation
  • Long-term memory and continuity — knowledge graphs, session diaries, cross-session resume
  • Tool integration — calendar, email, finance, security alerts, custom workflows
  • Permissions, kill-switches, and human-in-the-loop boundaries from day one

Common when off-the-shelf chatbots can't carry business context across sessions, or when a workflow needs an assistant that actually remembers.

Agentic DevSecOps

Embedding AI agents into security operations — alert triage, posture audits, incident runbooks, and secrets hygiene.

  • Alert-triage agents that classify, escalate, or close SIEM and EDR alerts with documented reasoning
  • Runbook automation — incident response, posture audits, threat-intel triage
  • DevSecOps pipeline integration — secret scans, dependency reviews, IaC security checks
  • Every agent action logged, reasoned, and reversible by design

Common when the SOC or DevSecOps team is drowning in alerts that need a reasoning layer, not just better filters.

AI Security Guardrails

Prompt-injection defense, output validation, tool-call governance, and permission boundaries for agentic systems.

  • Prompt-injection detection on untrusted inbound content (email, web pages, third-party tool output)
  • Output validation and PII/secrets egress checks before agent responses leave the boundary
  • Tool-call governance — least-privilege scoping for what each agent can actually do
  • Audit logging, kill-switches, and red-team simulation against your own deployed agents

Common before — or after — deploying an AI agent that touches customer data, sends external messages, or holds permissions you'd hesitate to grant a junior employee.

Vibe Coding Enablement

Helping engineering teams work effectively alongside AI coding agents — adoption, code review, and team norms.

  • Workflow adoption for Claude Code, Cursor, Copilot, and in-house agentic tooling
  • Code-review patterns for AI-generated code — correctness, security, drift, hidden assumptions
  • Team norms — when to delegate, when to write by hand, how to keep engineering judgment sharp
  • Skill development — prompt design, agent debugging, and durable human-AI collaboration habits

Common when the team is using AI coding tools ad-hoc but unevenly, or when leadership wants the productivity wins without the quality regressions that show up six months in.

Every engagement is scoped to your environment, your threat model, and your team — not a templated vendor playbook. If any of the above fits something you're working on, get in touch.

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