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.

Principle 1

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 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

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 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 Cannot Be Delegated

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

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

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