AI Security Assessment
Review AI apps, copilots, agents, RAG flows, tool use, prompt exposure, sensitive-data handling, and runtime controls.
- AI threat model and abuse cases
- Runtime guardrail recommendations
- Data-flow and evidence review
Security Consulting
Practitioner-led reviews for AI, cloud, and application security.
Focused reviews for teams shipping AI and cloud software: threat models, exposure prioritization, data protection gaps, control plans, and executive-ready summaries.
- AI security
- Threat modeling
- Exposure prioritization
- Control evidence
Packages
Focused engagements with concrete deliverables.
Secure Design Review
Map threats across trust boundaries, identity flows, integrations, data stores, policy owners, and operating dependencies.
- STRIDE-style threat model
- Risk-ranked mitigation backlog
- Architecture and control decision notes
Cloud and AppSec Review
Assess cloud posture, identity, logging, network boundaries, app attack surface, APIs, exploitable exposure, and release controls.
- Cloud hardening plan
- Risk-ranked exposure priorities
- Engineering-ready remediation guidance
Incident Readiness
Prepare playbooks, tabletop scenarios, escalation paths, control-owner workflows, communications flow, and decision criteria.
- Tabletop exercise
- Playbook, owner, and governance updates
- Executive response summary
How We Work
Short discovery, deep review, practical handoff.
Scoped around what is shipping, what must be protected, and what engineering teams can implement.
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01
Scope
Confirm systems, data classes, users, dependencies, current controls, exposure sources, and success criteria.
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02
Review
Analyze architecture, runtime flows, cloud posture, sensitive-data paths, implementation evidence, and response readiness.
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03
Prioritize
Separate urgent exposure from hardening work and map each item to an owner-ready control backlog.
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04
Handoff
Deliver the report, executive narrative, remediation plan, evidence map, and recommended product automation path.