The wider AI (Artificial Intelligence) use spreads across an organisation, the harder it becomes to establish which AI systems are actually in use, what data they process, and whether all of it sits under appropriate oversight.
AI arrives through numerous routes, from applications the marketing team adopts and customer service chatbots through to AI features bundled into existing enterprise software and free tools employees pick up without any formal process.
The result is shadow AI, and with it heightened exposure around data, security, compliance, and the quality of decisions being made.
This is where AI TRiSM (AI Trust, Risk and Security Management) comes in, helping organisations apply controls and oversight in a far more structured way.
The Problem of Unmonitored AI Systems
AI rarely enters an organisation through a single controlled entry point.
Adoption spreads across departments. Each team picks up whatever tool answers its own requirement, with no central coordination, so nobody ends up holding the complete picture.
AI gets embedded into existing systems. Software vendors increasingly add AI capability to their products through routine updates. Organisations often have no idea that a system they have run for years now processes data through new algorithms.
Employees use free tools. Staff frequently turn to freely available AI tools to get through daily tasks, sometimes pasting internal data into systems entirely outside company control.
No formal registration process exists. Without a registration mechanism, new systems arrive having passed through neither risk assessment nor compliance verification.
Together these four conditions leave organisations carrying risk whose scale nobody can quantify.
What AI TRiSM Contributes to Oversight
AI TRiSM is a framework developed by Gartner for managing trust, risk, and security across artificial intelligence systems through technical controls that enforce policy.
Its principal contribution to oversight lies in delivering comprehensive visibility. The framework does not stop at written policy. It extends to the technical mechanisms needed to discover what is running, assess how risky each system is, and monitor behaviour on an ongoing basis.
That approach differs from conventional security controls, which were designed to govern access and data traffic. AI systems require oversight of the outputs they generate and the decisions they reach, which falls outside what traditional security tooling was built to handle.
Six Steps to Overseeing Your AI Systems
Comprehensive oversight can be established through the following steps.
Step 1: Discover Everything That Is Running
Start with thorough mapping drawn from several sources at once. Work through active software subscriptions, review technology procurement records, survey each department about the tools they use, and check feature updates on systems already in place.
Discovery should cover three categories: internally developed systems, third-party platforms, and AI capability embedded within software adopted previously for other reasons.
Tahap 2: Klasifikasikan Berdasarkan Tingkat Risiko
Not every system warrants the same degree of oversight. Group them according to their impact on customers, employees, and regulatory compliance.
High-impact systems include anything influencing financial decisions, service access, or assessments of individuals. Low-impact systems cover internal productivity tools with no direct customer interaction.
Step 3: Map the Data Flows
Document what data each system processes, where it originates, where it is stored, and who has access to it.
Pay particular attention to systems handling personal customer data or sensitive business information, especially where processing involves third-party infrastructure.
Step 4: Apply Proportionate Controls
Match controls to each system's risk level. High-impact systems warrant layered approval, periodic testing, and comprehensive logging of every decision.
Controls typically cover data access restrictions, defined limits on what actions a system may take, customer disclosure obligations, and escalation routes to human review.
Step 5: Monitor Behaviour Continuously
Oversight does not end at initial approval. Live systems need ongoing behavioural monitoring, since accuracy and decision quality can shift as data patterns change.
Monitoring covers output accuracy, emerging bias, escalation frequency to human agents, and adherence to applicable policy.
Step 6: Run Periodic Audits
Schedule a comprehensive review across all systems at least annually, more frequently for high-impact ones.
Audits should verify that the inventory remains current, evaluate whether existing controls are working, and identify any systems that entered without going through the formal process.
Want full visibility over the AI systems running across your organisation? Get in touch with the KPSG team to discuss AI governance through our CXaaS solutions. Consultation is free of charge. [Schedule a Free Consultation.]
Warning Signs Worth Watching For
The following conditions indicate that oversight needs tightening without delay.
Customer complaints about decisions nobody can explain. When teams cannot articulate the reasoning behind an automated decision, logging is inadequate.
Noticeably different outcomes across customer groups. Decision patterns that consistently disadvantage a particular segment point to bias that needs investigating.
Accuracy declining for no obvious reason. Performance degrading over time usually traces back to shifting data patterns that the model has not been adjusted for.
Discovering unregistered systems. Finding AI tools that have been running without the governance team's knowledge shows the registration process is not working.
Discovering unregistered systems.Being unable to answer regulator questions. Struggling to produce documentation when authorities request it reveals a serious gap in oversight practice.
Tooling That Supports Oversight
The following technical capabilities make effective oversight considerably easier.
A central system catalogue. A database recording every AI system alongside its attributes, covering ownership, risk level, data processed, and approval status.
Automated logging. Systems that automatically record every significant decision along with the data behind it, producing an audit trail that can actually be followed.
A monitoring dashboard. A single view presenting performance indicators across all AI systems in real time, making anomalies easier to catch early.
Automated alerting. Mechanisms that trigger notification when a system behaves outside defined thresholds, covering accuracy drops and escalation spikes alike.
A registration procedure for new systems. A formal path every new AI deployment must follow, covering risk assessment and compliance verification.
Common Oversight Mistakes
The following errors frequently undermine oversight in practice.
Focusing only on internally built systems. Third-party systems remain the responsibility of the organisation using them, so they belong within scope.
Treating oversight as a one-off exercise. AI systems are dynamic, so initial approval offers no guarantee of consistent performance over time.
Leaving oversight entirely to the technology team. Assessing customer impact and regulatory compliance requires perspective from legal, compliance, and operations.
Ignoring free tools employees adopt. Unofficial tool use carries the highest risk precisely because it sits completely outside company control.
Applying uniform controls across every system. A disproportionate approach slows innovation on low-risk systems while potentially leaving high-impact ones insufficiently governed.
Conclusion
Overseeing an AI estate calls for a systematic approach, beginning with discovering what is running, classifying by risk, mapping data flows, applying proportionate controls, monitoring behaviour, and auditing periodically.
AI TRiSM brings these six steps together into a single coherent framework. The visibility it produces allows organisations to manage risk proactively rather than reacting once problems have already surfaced.
KPSG brings more than 35 years of experience in contact centre operations and customer experience management. We apply AI TRiSM-aligned principles across our CXaaS solutions, ensuring every AI system in operation carries adequate oversight so the technology delivers value without introducing risk nobody is controlling.
Interested in how CXaaS and BPaaS solutions can improve your operational efficiency and service quality? Contact us here.i. Contact us here. Watch more discussions and insights on customer experience, technology, and business transformation here
FAQ (Frequently Asked Questions)
How do you find AI systems nobody registered?
Discovery works best through several routes at once, covering software subscription records, procurement history, departmental surveys about tools in use, and checks on feature updates to existing systems.
Does every AI system need the same level of oversight?
No. Oversight should be proportionate to risk. Systems influencing financial decisions or customer service access warrant far tighter controls than internal productivity tools.
How often should AI systems be monitored?
Behavioural monitoring should run continuously through dashboards and automated alerts, while comprehensive audits happen at least annually, more frequently for high-impact systems.
Who is accountable for AI oversight?
Accountability is cross-functional, spanning technology, legal, compliance, information security, and operations, with a single owner designated at leadership level.
Do vendor AI systems need oversight?
They do. Responsibility for a system's impact stays with the organisation using it, so third-party systems must meet the same oversight standards as those built internally.
