Identity and access
False or compromised identities, excessive privileges and weak trust between organisations.
Could every actor, device and decision be authenticated and constrained?
SENTINEL’S ADVERSARIAL LENS
The Bad
Actor View.
I examine where trust could be exploited—and what you should notice before a weakness becomes a safety event.
DEFENSIVE ANALYSIS
Defensive scope
This section identifies attack surfaces, safety consequences and assurance questions. It deliberately excludes exploit procedures, payloads, target details and operational attack steps.
WHERE THE ADVERSARY LOOKS
Trust is the attack surface.
The highest-risk weaknesses often sit between systems rather than inside one component.
Data and sensors
Corrupted, withheld, stale or misleading inputs entering the safety picture.
Can provenance, timeliness and contradictions be detected?
Navigation and communications
Spoofed, jammed, delayed or unavailable positioning and connectivity.
Can the operation recognise degradation and move safely to a fallback?
AI/ML lifecycle
Poisoned training data, evasive inputs, model drift or a compromised model supply chain.
Are models, data and changes traceable, tested and monitored?
UTM and service interfaces
Weak APIs, third-party dependencies and trust that crosses organisational boundaries.
Which dependencies can change a safety-critical decision?
Human decision-making
Social engineering, alert flooding, automation bias and misleading explanations.
Will the human notice when the system is confidently wrong?
Governance and incentives
Ambiguous responsibility, commercial pressure and evidence gaps after an event.
Who owns the risk, the control and the record?
WHAT TO LOOK OUT FOR
Signals that deserve a second look.
No single signal proves malicious activity. The concern grows when anomalies combine, persist or appear around a change in access, software, suppliers or operating context.
- 01A sudden change in data distribution or sensor agreement
- 02An identity, privilege or device behaving outside its normal role
- 03Model confidence rising while evidence quality is falling
- 04Unexplained route, timing or conformance deviations
- 05Repeated alerts that condition people to ignore or disable a control
- 06Supplier or software changes without renewed assurance
- 07Missing logs, broken provenance or evidence that disappears at a boundary
DESIGN FOR RESILIENCE
Expect degradation.
- Authenticate identities and apply least privilege.
- Track data and model provenance across suppliers.
- Use independent signals and contradiction checks.
- Provide safe degraded modes and explicit escalation paths.
KEEP LEARNING
Test the assumptions.
- Red-team safely within authorised, isolated environments.
- Monitor drift, abuse cases and changes in operating context.
- Preserve evidence and learn across organisational boundaries.
- Keep accountable humans able to challenge automation.
PRIMARY EVIDENCE
NIST · Adversarial Machine Learning taxonomyEASA · Information Security (Part-IS)ICAO · UTM Framework, Edition 4NIST · AI Risk Management FrameworkNIST’s adversarial-ML taxonomy informs the AI attack-surface framing; EASA Part-IS and ICAO UTM material inform the broader information-security and operational-resilience context.