Capability ≠ authority
A system’s ability to act does not establish permission to act.
Research initiative · Foundational framework
Trust autonomy.
Verify everything.
A practical research framework for connecting autonomous decisions to observable evidence, defined controls, and accountable human oversight.
Our position: trust is not a feeling or a one-time certification. It is a claim that should remain testable throughout the system lifecycle.
ASSURANCE TRACE / 001
Evidence linked
DECISIONVERIFIEDtraceable outcome
01IDENTITYWho or what acted?
02INTENTWhat was authorized?
03EXECUTIONWhat occurred?
04OUTCOMEWhat changed?
Autonomous systems are moving from tools that recommend to systems that decide and act.
Assurance must move with them.
01 / THE CHALLENGE
Autonomy creates a new evidence problem.
Traditional controls often prove that a system was designed or configured correctly at a point in time. Autonomous systems change behavior through context, learning, updates, and interaction.
That creates a harder question: Can we reconstruct why a system acted, determine whether the action stayed within authority, and prove that safeguards worked?
Logging ≠ evidence
Data becomes useful evidence only when its origin, integrity, and meaning are clear.
Performance ≠ trust
Accuracy alone cannot prove accountability, resilience, or acceptable behavior.
02 / PROPOSED FRAMEWORK
Five claims. One continuous assurance case.
Select each claim to see the question it answers and the evidence it requires.
Identity is established.
Can every human, service, model, sensor, and machine involved in a decision be uniquely identified?
Representative evidence
- Workload and device identities
- Cryptographic provenance
- Authenticated human authority
Result Actions can be attributed to a known actor or component.
03 / ASSURANCE LIFECYCLE
Evidence before, during, and after action.
- 1
DEFINE
State the claim
Specify intended behavior, authority, and acceptable limits. - 2
INSTRUMENT
Make it observable
Design telemetry and provenance into the system. - 3
TEST
Challenge the claim
Evaluate normal, degraded, adversarial, and recovery conditions. - 4
DECIDE
Evaluate evidence
Compare results with policy and defined risk thresholds. - 5
REASSESS
Keep trust current
Reverify after change, drift, failure, or new context.
04 / RESEARCH AGENDA
Questions worth proving.
PRIMARY QUESTION
What evidence is sufficient to justify trust in an autonomous action?
How should authority be expressed so a machine can enforce it without erasing human accountability?
How can evidence remain trustworthy across cloud, edge, sensor, model, and physical-system boundaries?
When should a system degrade, defer, stop, recover, or transfer control?
How do we communicate residual uncertainty to operators, leaders, regulators, and the public?
05 / RESEARCH CONTEXT
A distinct layer in a connected body of work.
Verifiable Trust for Autonomous Systems
Defines the claims, evidence, and governance needed to justify trust.
Trusted Autonomous System
Explores how trusted behavior can be engineered into a working system.
Autonomous Micro Data Center
Tests resilient autonomy at the cloud-edge and physical-infrastructure boundary.
Responsible Cloud
Connects technology value, managed risk, and proof of responsible action.
THE STANDARD OF TRUST MUST RISE WITH THE LEVEL OF AUTONOMY.