Four patterns we see most often.

Forty-engineer SaaS team, customer interest from enterprise buyers blocked behind security questionnaires. No internal security lead. Existing AWS footprint with growing AI feature surface.

• Two-week posture diagnostic across application, cloud, and identity layers
• SOC 2 readiness gap analysis and remediation sequencing
• AI feature threat model for the new copilot integration
• Three-month vCISO retainer to run the SOC 2 audit cycle

Series-A AI product team shipping a copilot built on foundation-model APIs. Concerned about prompt injection, sensitive-data exfiltration, and customer-facing liability before public release.

• Threat model of the copilot's data flow and tool-use surface
• Adversary testing: prompt injection, jailbreaks, training-data extraction attempts
• Acceptable-use policy and incident-response playbook for AI-specific failure modes
• Vendor review of the foundation-model contract and DPA

Hundred-person professional services firm, post-incident, lacking internal security leadership. Insurance carrier requiring a remediation plan and ongoing program oversight as a condition of renewal.

• Incident-response review and lessons-learned facilitation
• Roadmap acceptable to the insurance carrier and the board
• Quarterly program operation: policy, vendor reviews, audit prep, board reporting
• Tabletop exercises to rebuild internal incident-response muscle

Forty-attorney litigation practice exploring an in-house alternative to commercial legal AI tools. Strict privilege concerns, client-data isolation requirements, and a partner-level mandate that no client matter ever leave the firm's tenancy.

• Threat model of the document corpus, retrieval pipeline, and privilege boundaries
• Architecture with per-matter data isolation and audit logging on every retrieval and generation
• Prompt-injection defenses against adversarial filings and embedded-document attacks
• Production handoff with red-team report and a partner-readable governance policy