AI Risk Management Automation: Prove Control, Move Faster.
Built for teams operationalizing AI at scale under real-world constraints. We automate the hard parts of AI risk & compliance—model inventory, impact assessments, evaluations, controls, and evidence—so you can ship responsibly without slowing the roadmap. Unlike static policy decks, our playbooks wire directly into your MLOps/DevOps stack and translate standards and laws into repeatable workflows your engineers actually use.
Benefits
- Continuous assurance → fewer fire drills — Controls, tests, and evidence collect themselves as you build and ship; auditors and counsel see live status instead of slideware.
- Faster assessments → shorter time-to-launch — Prebuilt AI-specific risk templates adapt to your systems, cutting weeks from DPIAs/AIAs, red-teaming, and sign-off cycles.
- Framework crosswalks → one effort, many obligations — Map once to multiple frameworks (risk, security, privacy, safety) so product teams don’t duplicate work for every standard.
- LLM-specific safety → fewer surprises in production — Built-in tests for prompt injection, sensitive data leakage, excessive agency, and insecure output handling catch issues early.
- Governance people can see — Dashboards for engineering, product, legal, and execs show model risk posture, outstanding actions, and trendlines over releases.
- Vendor & third-party coverage — Track model and data supply chains, require attestations, and attach upstream evaluations to your records of processing and risk register.
How It Works
- Assess
We start by making the invisible visible. In week one, we inventory AI systems (LLMs, classifiers, recommenders, vision, voice), data flows, and integrations across products and internal tools. We baseline risk posture and obligations for the jurisdictions you operate in, then prioritize what to automate first. Deliverables:- AI system & model inventory — Source models, fine-tunes, RAG pipelines, prompts, tools/actions, datasets, and providers; data categories, retention, and residency.
- Obligations profile — A practical list of what applies to you (governance, transparency, safety, documentation, testing) with target evidence for each function/role.
- Pilot plan — A four-week pilot that targets 2–3 systems, defines success metrics (assessment cycle time, evidence completeness, escape rate), and sets a go/no-go gate.
- Implement
We wire an automation layer on top of your build/test/release process so risk work happens as a by-product of engineering:- Policy → workflow — Turn your AI policy into Git-versioned checklists with gates in CI/CD. Ship prompts, datasets, and configs with metadata that drive assessments and evidence automatically.
- Impact assessments — Auto-populate DPIAs/AIAs from system metadata, data inventories, and use-case catalogs; route to legal/privacy for targeted review instead of 0→1 authoring.
- Evaluations & red-teaming — Run curated test suites (safety, fairness, robustness, grounding) on every change. Schedule adversarial tests for LLM-specific risks and capture findings with severity/owner/ETA.
- Controls & auditability — Enforce role-based access, data minimization, approval thresholds for tool actions, and retention defaults. Emit tamper-evident logs (prompts, retrieved context, tool calls, results).
- Evidence factory — Generate living “System Cards” and “Model Cards,” risk registers, trace bundles, and decision logs from your telemetry—ready for regulators, auditors, and customers.
- Integrations — Jira/ServiceNow for work items, GitHub/GitLab for PR checks, Datadog/Prometheus for runtime KPIs, Snowflake/BigQuery for evaluation storage; Slack/Teams for reviews and alerts.
- Optimize
Governance isn’t one-and-done. We tune weekly: reduce manual steps, raise quality bars, and expand coverage once the pilot’s KPIs hold. Over time we:- Scale assessments with reusable templates for new systems and major changes.
- Harden safety: raise confidence thresholds, narrow tool scopes, update prompt hardening, and add guardrails as models evolve.
- Refresh crosswalks when standards or laws update, keeping your controls aligned without ground-up rewrites.
Case Snapshot
Anonymized example: A multi-product platform team automated AI inventory, AI impact assessments, and LLM safety tests across three customer-facing features. In six weeks they cut assessment lead time from “several weeks” to “days,” closed long-standing action items (prompt injection hardening, output validation), and shipped living system documentation tied to releases. Legal now approves based on evidence, not ad-hoc screenshots; engineering spends less time gathering artifacts and more time reducing real risk.
Risk Reversal
Start with a 4-week pilot; continue only if KPIs are met. Day-0 baseline, day-14 checkpoint, day-28 readout. If jointly agreed targets (assessment time, evidence completeness, test coverage, escaped-issue rate) aren’t reached, stop—no long-term commitment. The program stays anchored to measurable risk reduction, not paperwork.
FAQ
Which frameworks and regulations do you support?
We align controls and evidence to widely adopted risk and governance references and risk-based regulations. Practically, that means your engineering work maps to recognized functions (governance, mapping, measurement, management), formal management-system requirements, and risk-specific guidance—without asking teams to fill five different forms for the same control.
How do you test LLM-specific risks?
We run suites for prompt injection, data exfiltration, insecure output handling, excessive agency, and system-prompt exposure; plus robustness and grounding tests for RAG (off-policy retrieval, citation integrity, stale source detection). Findings become tickets with owners and SLAs. Sensitive actions require approvals and least-privilege credentials.
What does “evidence automation” actually produce?
For each AI system: an up-to-date system card, risk register, assessment package, evaluation results, control mappings, and signed decision logs. For leadership: dashboards by product, model, and business unit that show risk posture, top issues, and trends over time.
Will this slow down our engineers?
No—the point is to remove friction. Checks live in CI/CD and PR templates; metadata is captured from code and configs; tests run with your fixtures; reviews happen in the tools you already use. The result is fewer bespoke questionnaires and faster, better decisions.
How does this help with regulators, customers, and auditors?
You’ll have consistent artifacts that answer the questions they actually ask: what the system does, where the data comes from, who can do what, how it was tested, what changed since last release, and where to see the logs. Because evidence is generated automatically, it stays current.
What about vendor models and third-party tools?
We track external models/tools in your inventory, ingest their system cards and security attestations, and attach contractual limits (purpose, data use, retention). If a provider updates behavior or terms, affected systems are flagged for review automatically.
What You Get
- Live AI system & model inventory with data maps and residency notes.
- Automated impact assessments (AIAs/DPIAs) and change-driven reassessments.
- Evaluation harness for safety, fairness, robustness, grounding, and LLM-specific risks—scheduled and on-demand.
- Policy-to-workflow controls: RBAC, redaction, retention, approval thresholds for tool actions, and guardrails baked into CI/CD.
- Evidence packs: system cards, model cards, risk registers, signed decision logs, and trace bundles tied to releases.
- Crosswalk library to major frameworks and risk-based laws so one control update propagates everywhere it applies.
- Dashboards for engineering, product, legal/PR, and executives with posture, gaps, and trendlines.
- Pilot readout with KPI deltas, rollout plan, and a risk register mapped to owners and timelines.
Get a Pilot Plan
Book a 30-minute scoping call. We’ll inventory 2–3 AI systems, wire automated assessments and tests, and deliver a four-week pilot with KPI targets, evaluation suites, and a go/no-go gate.
