The Secure AI 5 Principles

A practical leadership framework for safe, reliable, and value-driven AI. Use these principles to set strategy, measure progress, and turn governance into execution.

The Secure AI 5 Principles Diagram Calculate Your Secure AI Score
Principle 1
Trust icon

Trust Is the New Perimeter

Principle: Your organization's trust boundary now extends to every model you use, every dataset you touch, and every prompt your employees send.

Measures
  • Model performance SLOs, drift thresholds, and rollback criteria
  • User explanations, consent trails, and telemetry transparency
  • Security hardening and dependency trust for third-party models
Leadership Actions
  • Approve trust requirements in product charters
  • Fund independent validation and red teaming
Principle 2
Governance icon

Governance Must Move Faster Than Innovation

Principle: If your governance model can't keep up with the pace of AI innovation, you are governing a ghost.

Measures
  • Risk tiering by use case and control baselines per tier
  • Time-boxed approvals, auditable waivers, and policy coverage
Leadership Actions
  • Stand up an AI governance council with decision rights
  • Integrate AI risks into board risk reporting and audits
Principle 3
Design icon

Security by Design, Not by Audit

Principle: You cannot inspect security into an AI system; it must be built in from day one.

Measures
  • Adversarial and jailbreak test coverage
  • Prompt, agent, and pipeline change management
Leadership Actions
  • Adopt a secure SDLC for AI with release gates
  • Resource chaos testing and safe fallback UX paths
Principle 4
Data icon

Data Is the Weakest Link

Principle: AI is only as secure as the data that trains it, feeds it, and surrounds it.

Measures
  • Lineage coverage, data contracts, and PII masking rates
  • Dataset documentation and license attestations
Leadership Actions
  • Mandate DLP, access controls, and sovereign hosting where required
  • Back data minimization and consent-aligned retention
Principle 5
Accountability icon

Accountability Cannot Be Delegated

Principle: AI may automate decision-making, but it cannot automate accountability.

Measures
  • Named owners for models, prompts, and datasets
  • KPIs for value, risk, user impact, and post-incident learning
Leadership Actions
  • Assign single-threaded owners for high-risk use cases
  • Publish model cards and accountability logs

Secure AI Leadership Assessment Score

Evaluate how your organization measures up across the Secure AI 5 Principles. Your responses will generate an overall score and maturity band — helping you identify whether your Secure AI is Lagging, Maintaining, or Leading.

Assessment Questions

Trust icon Trust Is the New Perimeter 5 questions

Do we have a defined and measurable framework for AI trust and assurance across all business units?

Are external stakeholders (customers, regulators, partners) confident in our AI transparency and fairness?

Is independent validation or red-teaming used for our most critical AI systems?

Have we set clear thresholds for when an AI system is considered "untrustworthy" or high-risk?

Can non-technical leaders easily understand how key AI decisions are made and justified?

Governance icon Governance Must Move Faster Than Innovation 5 questions

Does our governance model keep pace with the speed of AI development and deployment?

Is there a named executive or committee accountable for approving all high-impact AI use cases?

Are AI risks formally integrated into our enterprise risk, compliance, and audit reporting?

Have we defined "responsible AI" in measurable business terms rather than as a policy statement?

Does an AI governance council exist with authority to delay or halt initiatives when risks exceed thresholds?

Design icon Security by Design, Not by Audit 5 questions

Are AI security, privacy, and ethics embedded into the design process rather than added later?

Are cross-functional teams (risk, legal, security) involved early in AI product design decisions?

Are performance incentives aligned to build safe AI systems, not just fast ones?

Do we systematically capture and act on lessons learned from AI incidents or failures?

Are human override and safe fallback mechanisms built into AI experiences?

Data icon Data Is the Weakest Link 5 questions

Do we have full visibility into where our data originates and how it is used in AI models?

Is data quality, bias, and leakage recognized as a core AI risk in our organization?

Are data storage and processing decisions aligned with jurisdictional and sovereignty requirements?

Have we assessed reputational risks related to the use of customer or employee data in model training?

Is data lineage and integrity reporting visible at the same level as financial or operational metrics?

Accountability iconAccountability Cannot Be Delegated 5 questions

Does every major AI system have a named executive accountable for its operation and outcomes?

Do we have clear escalation and ownership processes for AI-related incidents or harm?

Are we tracking both business value and social or ethical impact of AI systems?

Is accountability for AI performance and integrity embedded in leadership objectives and culture?

Could we confidently explain to regulators, auditors, or the public who approved each major AI system and why?

Calculate Your Secure AI Score