AI threat modeling
Injection, exfiltration, poisoning and abuse scenarios mapped and mitigated by design.
Engineering Excellence
AI Security & Compliance
Design AI systems that satisfy security teams and regulators without blocking delivery velocity.
- 100%of AI data flows mapped and controlled
- SOC 2GDPR and HIPAA-aligned delivery
- 0standing credentials in AI runtimes
Capabilities
Security for systems that think
SOC2-ready AI systems with RBAC, audit trails, and enterprise governance.
Identity and access for AI
Least-privilege identities for agents and pipelines — scoped, short-lived, auditable.
Data protection
Classification, encryption, redaction and residency controls across every AI data path.
Compliance engineering
Controls and evidence automation for SOC 2, GDPR, HIPAA and internal risk frameworks.
How it works
Ship AI your CISO can sign off on
AI introduces attack surfaces traditional security programs don't cover. We close the gap — without stalling delivery.
New threat surface
Defenses for AI-specific attacks
Prompt injection, tool abuse, training-data leakage, model exfiltration — we threat-model your actual system and build layered mitigations, then red-team them.
- Threat models covering the full AI attack surface
- Layered runtime controls, not prompt-based hopes
- Red-team validation with findings as regression tests
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Identity and data
Least privilege, even for agents
Every agent and pipeline gets a scoped, short-lived identity; every data flow is classified, encrypted and residency-aware. What the AI can reach is exactly what it needs — nothing more.
- Scoped service identities with automatic rotation
- PII classification and redaction across pipelines
- Data residency and retention enforced per jurisdiction
DocsCRMWiki
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Answer with citationsscoped to the user's permissions [1] [2]
Compliance
Evidence generated, not assembled
Controls are implemented in infrastructure and pipelines so compliance evidence accumulates automatically — audits become exports, not archaeology.
- Control mapping to SOC 2, GDPR, HIPAA frameworks
- Automated evidence capture from pipelines and logs
- Audit-ready documentation maintained continuously
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Use cases
Where security & compliance delivers value
AI security assessments
Current AI systems threat-modeled, tested and hardened.
Secure AI architecture
New systems designed with security as a first-class requirement.
Compliance programs
SOC 2 and regulatory readiness for AI-heavy products.
Data governance for AI
Classification and control of what models can see and say.
Vendor AI risk
Third-party AI tools assessed and gated before enterprise rollout.
Incident preparedness
AI-specific detection, response runbooks and tabletop exercises.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Security had blocked every AI initiative for a year — rightly, given what was proposed. This was the first architecture our CISO approved on the first pass, because the controls were in the design, not the appendix.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Vault
- Okta
- AWS IAM
- Azure Entra
- OPA
- Presidio
- Wiz
- Splunk
- Datadog
- CrowdStrike
- Terraform
- Kubernetes
FAQ
Common questions
What's actually different about securing AI systems?
Three things: inputs are adversarial by nature (prompt injection), the system can act (tool abuse), and data flows through models in new ways (leakage, memorization). Each needs controls traditional appsec doesn't provide — enforced in the runtime, not the prompt.
Can our data end up training someone's model?
Not if the architecture prevents it. We configure zero-retention API terms, private endpoints or self-hosted models depending on sensitivity — and document the data path so you can prove it.
Will security controls slow our AI teams down?
Ours are built into the platform: pre-approved paths, automated checks, self-serve access with guardrails. Teams move faster because security review becomes a pipeline stage, not a committee.
Do you help with the EU AI Act and similar regulation?
Yes — risk classification, required documentation, human-oversight requirements and technical controls, mapped into your delivery process so compliance is continuous rather than a scramble.
Next step
Secure your AI before it scales
Get an AI-specific security assessment — and an architecture your risk team will approve.