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DiscoverMap the risk surface
Find where AI, vendor tools, data flows, and cyber exposure already touch regulated work before the organization scales around unknown risk.
Governance & Risk
Govern AI before risk governs you.
Life sciences teams are adopting AI through platforms, vendors, copilots, agents, and business-led pilots. USDM helps turn that pressure into a governed operating model: clear ownership, risk-based controls, cybersecurity alignment, third-party oversight, validation discipline, and evidence your teams can defend.
Risk reality
The governance perimeter moved.
Business teams using public or embedded AI before formal approval exists
Vendors adding AI features without clear regulated-use boundaries
Cybersecurity, Quality, Regulatory, IT, and Procurement reviewing risk in separate lanes
Policies that describe responsible AI but do not create workflow-level evidence
The answer is not a policy binder. It is a working governance system that connects AI, cyber, vendor oversight, data, validation, and business accountability.
Layer 0–5 governance strategy
Build governance in layers, not after the mess arrives.
USDM uses a layered AI operating model to move organizations from visibility to control to scale. Governance is not a separate workstream sitting beside AI. It is the structure that lets AI, cyber, vendor oversight, data integrity, and regulated workflows operate together.
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ControlSet governance guardrails
Define intended use, ownership, policy, risk classification, approval paths, and escalation rules so teams can move without improvising controls.
2
PrepareMake systems and vendors governable
Align cybersecurity, platform access, third-party oversight, data lineage, and evidence expectations around the workflows AI will affect.
3
OperateEmbed controls into workflows
Turn policy into review gates, human accountability, audit trails, monitoring, vendor controls, and change discipline inside daily operations.
4
DefendGenerate evidence as work happens
Create inspectable records for decisions, approvals, exceptions, supplier reviews, model changes, access changes, and control performance.
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ScaleContinuously govern the portfolio
Monitor drift, incidents, new use cases, vendor changes, cyber signals, adoption, and control effectiveness as the AI portfolio grows.
The governance system
The work is visibility, ownership, controls, and evidence.
Strong governance gives teams a way to say yes safely. It creates the pathway for high-value AI and automation while making risk visible enough to manage across Quality, Regulatory, Clinical, Manufacturing, IT, Security, Procurement, Legal, and executive leadership.
Inventory + intended use
Create a living view of AI use cases, third-party tools, system touchpoints, regulated impact, and business ownership.
Risk classification
Segment use cases by GxP impact, data sensitivity, decision criticality, automation level, vendor dependency, and cybersecurity exposure.
Accountable operating model
Clarify who approves, who operates, who reviews, who owns exceptions, and when Quality, Regulatory, Security, Legal, and business leaders engage.
Evidence architecture
Define the artifacts, audit trails, review records, validation evidence, vendor records, and monitoring signals needed to defend use over time.
Control areas
One governance architecture across AI, cyber, vendors, and validation.
The risk does not arrive neatly by department. USDM helps connect the controls so the organization can govern the full operating environment, not just one policy domain at a time.
AI governance
Policies, SOPs, risk tiers, human oversight, approved-use boundaries, training expectations, and workflow controls for responsible adoption.
Cybersecurity
Secure-by-design practices, access governance, vulnerability management, incident readiness, platform oversight, and FDA-aligned cyber evidence.
Third-party risk
Vendor AI disclosure, supplier risk segmentation, continuous monitoring, contract/control alignment, and defensible oversight of partner ecosystems.
Validation + change control
Risk-based validation scope, CSA/CSV alignment where appropriate, release discipline, model/vendor change review, and lifecycle documentation.
What changes for the business
Governance becomes the path to AI value, not the department of no.
The point is not to slow AI down. The point is to make the highest-value use cases safe enough, clear enough, and evidence-backed enough to scale. That means fewer hidden pilots, fewer vendor surprises, better inspection readiness, and more confidence from the teams expected to use the technology.
Clear visibility into AI, vendor, cyber, and regulated workflow risk
Controls that support adoption instead of freezing the business
Evidence trails that Quality, Security, Regulatory, and executive teams can review
A scalable operating model for governed AI across domains and platforms
Deep dives
Keep going where the risk is highest.
AI Governance for Life Sciences: Enterprise Framework
A practical governance framework for lifecycle controls, vendor AI risk, citizen development, and responsible adoption.
Read moreThird-Party Risk Management in Life Sciences
How regulated organizations can strengthen oversight as suppliers, CROs, and technology partners introduce new operating risk.
Read moreAI Governance + Compliance in Life Sciences
How governance becomes workflow design: review points, evidence capture, human accountability, and defensible AI operations.
Read moreFrequently Asked Questions
Questions leaders ask before they move.
Where should AI governance start?
Start with visibility: current AI use, vendor AI exposure, regulated workflow touchpoints, data sensitivity, and business ownership. You cannot govern what nobody has mapped.
How does governance connect to cybersecurity?
AI expands the attack surface through platforms, data movement, access patterns, third-party tools, and automated workflows. Cybersecurity must be part of the AI operating model, not a late-stage review.
What makes governance defensible in life sciences?
Defensibility comes from risk-based decisions, clear ownership, controlled workflows, training, validation where appropriate, monitoring, and evidence generated as the work happens.
Talk to a risk specialist
Build governance that holds up under scrutiny.
USDM helps regulated organizations design risk frameworks, manage third-party vendors, and maintain cybersecurity postures that satisfy regulators and auditors.
- Third-party risk management and vendor qualification
- vCISO and cybersecurity services for life sciences
- GxP audit readiness and remediation
- Risk-based governance frameworks