AI automation — secured before it's deployed.
Workflow automation with built-in security review, audit trails, and tenant-isolated agents. We deploy, you own. No shadow-IT agents leaking data to vendor clouds.
Companies looking to apply AI to operational workflows — ticket triage, email summaries, CRM sentiment, call transcription, executive reporting — without giving up security review, audit trails, or data-handling discipline. Especially relevant when leadership wants AI but the security team wants guardrails first.
AI automation engagement scope
Use-case + risk review
What you want to automate, where the data lives, what regulatory scope it touches, what failure modes matter. Before any agent is built.
Tenant-isolated deployment
Agents run in your Microsoft / AWS / Cloudflare tenant. Not in vendor SaaS multi-tenancy. Logs, embeddings, and prompts stay in your account.
Audit trail by default
Every agent action logged with input, output, model version, latency, and the human or trigger that initiated it. Exported to your SIEM.
Guardrails + escalation
PII / PCI / PHI detection before model calls. Refusal patterns for out-of-scope requests. Explicit human-in-the-loop for actions that move money, change permissions, or send external comms.
Cost governance
Per-tenant, per-use-case spend limits with alerting. Token-budget reviews monthly. Model-tier optimization (you don't need GPT-5 for ticket triage).
Documentation + handoff
Architecture diagrams, prompt-engineering rationale, refusal patterns, runbooks for failure modes. Yours to keep, modify, or move.
Items below sit outside the scope of this service. Some are handled by separate EFROS engagements; others belong with your existing partners or in-house team.
- Custom large-language-model training on your data (we wire to existing models)
- AI safety research or alignment work
- Compliance attestation for AI use specifically (regulators are still defining this; we document operational controls)
- Replacement of CRM, helpdesk, or accounting platforms
Tenant-isolated AI workflows with explicit data-handling policy and audit logging close the most common deployment risks — accidental data exfiltration, prompt injection from untrusted content, lost human-in-the-loop on high-impact actions.
Maps to emerging AI controls in SOC 2 (data classification + handling), HIPAA Security Rule (where AI touches PHI), GDPR Art. 22 (automated decision-making), and the NIST AI Risk Management Framework. Cyber-insurance carriers are starting to ask about AI deployment posture; documentation comes out of this engagement.
Standard versions should be verified from the official source before contractual reliance.
Questions before we start.
Aren't AI agents inherently risky?
Unbounded ones are. Tenant-isolated agents with explicit guardrails, audit trails, and human-in-the-loop on high-stakes actions are no riskier than any other workflow automation — and considerably less risky than the 'just give Slack access to ChatGPT' patterns we keep finding in client environments.
What happens if the model vendor changes pricing?
Architecture decouples agent logic from model choice. If pricing on the current model shifts, we migrate to a comparable model (Claude → GPT, Llama, Mistral, etc.) without rewriting the orchestration.
Will employees just bypass this and paste data into ChatGPT?
Some will. The technical answer is DLP policy + Conditional Access + a real internal tool that's better than the consumer alternative. The policy answer is training and acceptable-use. Both required.
Start with your domain.
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