Research
New research findings, technological developments, attack techniques, innovations, and lessons learned
AAM-008
Continuous AI
Assurance
How do you ensure your AI remains trustworthy tomorrow?
An Assurance assessment is always valid for a specific context and a specific point in time. When models, data, systems, threats, or responsibilities change, the basis on which trust rests changes as well.
Continuous AI Assurance identifies such changes and asks the relevant questions again. New Evidence can strengthen trust – but it can also change Known Limitations or reveal previously unknown Uncertainty. This keeps the basis for trust open to review and capable of evolving.
Discuss Continuous AI Assurance →01
Why
AI does not stand still. New models, data sources, threats, regulatory requirements, or organisational changes can alter the assumptions on which an earlier assessment was based.
More Evidence does not automatically mean less Uncertainty. Sometimes new Evidence reveals what we did not know before. Continuous AI Assurance therefore goes beyond AI and model monitoring: it determines whether observed change requires existing Evidence, assumptions, or previous Assurance conclusions to be reassessed. This ensures that relevant questions are revisited at the right time.
Trustworthy AI is not an end state.
02
What
Five dimensions remain in motion.
Security
New threats, vulnerabilities, Red Team findings, architecture changes, identities, and supply chain
Governance
Organisational changes, policies, roles, Human Oversight, and regulatory requirements
Quality
Model and data quality, monitoring, drift detection, and continuous improvement
People
Training, awareness, acceptance, new AI use cases, and organisational changes
03
How
Continuous AI Assurance combines a robust Baseline with recurring observation and targeted Reassessments. We go deeper where changes affect existing assumptions or where new Evidence is required.
Baseline
Establish the current AI Assurance status and desired target state
Review
Regularly review changes across the AI landscape, Governance, and operations
Observe
Identify relevant changes in technology, the threat landscape, organisation, regulation, and Research
Reassess
Trigger targeted reviews or assessments when relevant changes occur
Improve
Prioritise findings with management and update the roadmap and actions
04
Knowledge
Continuous Assurance does not create a static certificate. It provides an up-to-date basis for orientation:
How your organisation's AI Assurance status and capabilities evolve over time
Which technological, organisational, or regulatory changes affect the existing basis for trust
Which new risks, vulnerabilities, or Uncertainties require early attention
Where Governance, Security, Quality, People, or Research should be reassessed
Which actions and deeper AAM Modules are appropriate in light of new Evidence
Typical deliverablesContinuous AI Assurance ReportAssurance BaselineEvidence ChangesReassessment FindingsUpdated PrioritiesAssurance Roadmap
05
scip AG
For us, AI Assurance is not a one-time assessment.
scip combines continuous Research, Cybersecurity, Governance, and practical assessment across the entire AI lifecycle. This enables us to evaluate new technological developments, attack techniques, organisational changes, and regulatory requirements early and challenge what they mean for existing systems.
Our goal is not to demonstrate trust once. We support organisations in keeping the foundations of their trust current, interpreting new Evidence, and asking the right questions again when relevant changes occur.
Continuous
Research
Lifecycle
06
Perspective
AAM is not a linear process. Depending on the context, other questions may become relevant and different capabilities can be applied where they add value.
AAM-002Is your AI architecture built to last?Architecture ReviewAAM-003Can your AI withstand realistic attacks?AI Red TeamAAM-004Are responsibility and governance for AI organised effectively?Governance & Operating ModelAAM-005Can you place justified trust in your AI?Trustworthy AI AssessmentAAM-006Can you trace how your AI influences decisions?Decision Security
07
Insights
Our Research treats Trust as dynamic: new Evidence can change what we know, reveal Limitations, and create new Uncertainty. Assurance therefore needs to remain capable of learning and adapting.
Eight Questions on the Path to Trustworthy AI – Trust Is Not a Phase →ISO/IEC 5338:2023 — AI System Life Cycle Processes →AI Assurance & Trustworthy AI →ISO/IEC 42005:2025 — AI System Impact Assessment →On Trust in AI – A Systemic Approach →08
Trust is not a state.
It must evolve with reality.
AI changes. Its environment changes. The basis for trust must therefore remain open to review and capable of evolving.
Discuss with scip →