03 Contain · AI Safety Assurance
Assess what happens when the system gets it wrong.
An independent assessment of failure modes, misuse, autonomy boundaries, oversight quality, safe operating limits and recovery. The purpose is not to make AI risk free. It is to show that material risk is identified, contained and reduced to a level the organisation can accept.
Assessment sequence
- 01Identify failure and misuse scenarios
- 02Establish consequence and who is affected
- 03Assess autonomy limits and approval gates
- 04Test whether oversight is meaningful
- 05Assess detection, rollback and recovery
Who this is for
Organisations where an incorrect AI output or action would cause real harm.
- Teams deploying AI into client-facing or decision-affecting workflows
- Organisations giving agents authority to act rather than only to draft
- Risk and compliance leaders assessing oversight quality
- Operational leaders who need clear limits for their teams
- Boards accountable for outcomes produced with AI assistance
Outcomes
What changes for the organisation
A tested failure picture
Documented failure and misuse scenarios with consequence and likelihood assessed against real use.
Defensible autonomy limits
A clear position on which actions require approval and which can proceed unassisted.
Oversight that is real
An assessment of whether human review changes outcomes or merely records them.
What Alacrix does
We test the system under the conditions it will actually meet.
Safety failures are rarely exotic. They come from ordinary pressure, poor context, unclear limits and review that has no time to be meaningful.
- Identify failure, degradation, fabrication and misclassification paths
- Assess foreseeable misuse and out-of-purpose use
- Exercise edge cases, poor inputs and ambiguous context
- Assess autonomy thresholds against consequence
- Test whether human gates change outcomes in practice
- Assess detection, rollback, correction and communication
Assessment domains
What we examine
Six safety domains applied to real operating conditions.
Failure modes
How the system degrades, fabricates, misclassifies or acts on incorrect context, and what depends on it being right.
Misuse and pressure
Foreseeable misuse by users under time pressure, and use of the system outside its intended purpose.
Autonomy boundaries
Which actions the system may take unassisted, and where consequence requires an approval gate.
Human intervention
Whether the reviewer has the information, authority and time to intervene, or is approving by default.
Safe operating limits
The conditions, data quality and volumes under which operation remains within acceptable risk.
Rollback and recovery
How an incorrect action is detected, reversed, corrected and communicated to those affected.
Deliverables
What you leave with
Independent, not self-assured
Acceptable risk, stated honestly.
We do not claim a system is safe in the abstract. We assess whether material risk is identified, controlled and reduced to a level an accountable owner can knowingly accept.
- No claim that AI use is risk free
- Residual risk is named and assigned to an owner
- Limits are written so operational teams can follow them
- This is an independent assurance opinion, not a certification or regulatory approval
Engagement process
How the work runs
- 01
Identify
Establish failure modes, misuse paths and who is affected when the system is wrong.
- 02
Test
Exercise the scenarios, the autonomy limits and the human intervention points.
- 03
Conclude
State residual risk, required limits and the conditions for acceptable operation.
Next step
Know the limits before production does.
An independent safety assessment establishes failure modes, autonomy limits, meaningful oversight and the route back from an incorrect action.