Imagine every adviser in your firm had a compliance officer sitting beside them.
The compliance officer watches every call, every click, every model output. It checks each one against your policies and regulation, in real time. It never gets tired, never samples, and always leaves a trail you can defend to a regulator.
That is the idea behind the Machine Line of Defence™.
In this, we unpack what the Machine Line of Defence™ (MLoD) actually is, why it matters for CROs, and how to picture it without getting lost in AI jargon. The core analogy is simple: a second line for machines.
From three lines of defence to a machine line
Most financial services firms still rely on the familiar Three Lines of Defence model:
- First line: business and operations teams who own and manage risk.
- Second line: risk and compliance who set policies and monitor adherence.
- Third line: internal audit who provide independent assurance.
This structure was built for human processes. Advisers give advice, underwriters approve loans, QA teams check a small sample of interactions after the event.
Now AI has entered the first line:
- Models screen transactions and communications.
- Assistants draft advice, letters and case notes.
- Emerging agentic systems can gather data, apply rules and produce recommendations with limited human input.
Human assurance alone cannot see enough or move fast enough. Sampling 5 percent of activity is not credible when an AI agent can touch 100 percent of cases in minutes.
The gap is clear. Firms need an assurance model that matches digital scale and speed. This is where the Machine Line of Defence™ comes in.
A digital mirror of human oversight
The Machine Line of Defence™ is a digital mirror of the oversight structure you already know.
Instead of human QA teams watching human advisers, you now have:
- AI systems that carry out work in the first line.
- AI systems that monitor those workers, human or machine, as a new digital second line.
- Human risk and audit functions above them, governing the whole picture.
In other words, the MLoD turns software into part of the control framework. It gives every important AI model or agent an assurance layer that observes, interprets and validates its outputs.
The aim is not to remove people from oversight. The aim is to extend human control into a world where part of the first line is now made of code.
Done well, this shifts oversight from slow, after-the-fact checking to a living, real time control system. Instead of waiting for quarterly QA reports, risk teams see issues as they emerge inside the data streams.
The “second line for machines” analogy
A simple picture can bring this to life.
- Imagine an AI adviser that recommends products to customers.
- Now imagine an AI compliance officer sitting beside it.
The adviser AI interacts with the customer, gathers information and proposes an outcome. The compliance AI watches every recommendation in real time, checking it against suitability rules, Consumer Duty outcomes and internal policy.
If something looks off, the oversight AI can:
- Flag the case for human review.
- Block the recommendation.
- Explain which rule or guideline was at risk.
This is a second line for machines. The adviser AI works in the first line. The oversight AI performs a second line role, at machine speed.
Aveni’s own assurance agent, Agent Assure, follows this pattern. It evaluates the outputs and behaviour of autonomous financial agents, monitors reasoning traces and outcomes, and escalates risks for human review.
The benefit is scale. An AI adviser can work directly with customers, while an AI auditor watches every interaction in parallel. Coverage moves from samples to populations.
→ Discover how Aveni’s agentic AI delivers assured automation in UK financial services
The three interaction layers MLoD must cover
Financial services now run across three main interaction types. The Machine Line of Defence™ needs to understand and monitor all three.
1. Human to human (H2H)
This is the traditional world:
- An adviser speaks to a client.
- A complaints handler manages a case.
- A supervisor coaches a team member.
MLoD does not replace human QA here, but it can enhance it. For example, Aveni Detect uses FinLLM and agentic workflows to analyse every call and document in a case, identify risk indicators such as vulnerability, and surface cases that need human judgement.
Human reviewers still make the decisions. The machine line gives them complete coverage and better triage.
2. Human to machine and machine to human (H2M / M2H)
These are hybrid interactions, such as:
- A customer chatting with a virtual assistant.
- An adviser using an AI decision support tool during a meeting.
- An operations team relying on AI summaries inside a CRM.
Here, assurance needs to cover both sides:
- Are humans using AI safely and following procedures?
- Are AI systems responding accurately, fairly and in line with policy?
The Machine Line of Defence™ can monitor the human behaviour (for example, risky data entry or missed disclosures) and the AI behaviour (for example, inaccurate explanations or off-label recommendations) in the same control view.
3. Machine to machine (M2M)
This is where traditional assurance models really start to stretch.
Examples include:
- A credit underwriting agent calling a separate risk scoring service.
- A transaction monitoring model feeding alerts into an AI triage system.
- Multiple agents cooperating to complete an end-to-end process such as onboarding or arrears support.
These workflows can run thousands of times before a human sees a single case.
In this layer, the Machine Line of Defence™ becomes the primary line of oversight. AI-driven control layers watch:
- The data flowing between systems.
- The decisions made at each step.
- The behaviour of the agents over time.
If an algorithm drifts from policy, another algorithm catches it and alerts the team or halts activity.
→ See how task-specific AI models deliver accuracy in regulated financial services environments
Turning data into fuel for oversight
How can an AI auditor watch an AI adviser or two algorithms supervise each other? The answer is in the data trails they generate.
Three types of data sit at the core of the Machine Line of Defence™.
1. Telemetry
Telemetry is the real time stream of events and logs from systems. It covers:
- When models are called.
- Which inputs they receive.
- Which safeguards trigger.
- How long they take to respond.
In an advice context, telemetry might include a transcript, timestamps and the sections of policy the system referenced. In trading or payments, it might be every order or transaction with its parameters.
Regulators in the UK and EU increasingly expect continuous model monitoring and robust record-keeping for important AI systems, especially those considered high risk under the EU AI Act.
2. Model reasoning and decision trails
Modern AI systems can expose structured information about how they reached a decision. This does not mean dumping full internal chain of thought. It means capturing enough structured reasoning to support oversight:
- Which customer factors influenced the outcome.
- Which policy rules were considered.
- Where the model saw potential harm or ambiguity.
Techniques such as semantic boundary detection can scan these reasoning traces to spot harmful patterns or bias. Oversight agents can replay a model’s logic, check that each step aligns with policy, and present an explanation to human reviewers.
3. Behavioural and outcome data
Finally, the Machine Line of Defence™ needs to see what happens next:
- How customers respond to AI recommendations.
- How advisers act when they receive AI suggestions.
- Whether particular groups are seeing worse outcomes.
This feedback closes the loop. If human reviewers frequently override an oversight agent’s alerts, the agent can be tuned. If certain products create clusters of poor outcomes, task definitions or policies can be updated.
By combining telemetry, reasoning and outcomes, the Machine Line of Defence™ turns raw data into evidence-backed assurance. This is exactly the type of demonstrable control regulators expect in an AI-rich environment.
→ Learn how FinLLM builds AI safety into financial services with comprehensive transparency frameworks
AI monitoring AI monitoring humans
One defining feature of the Machine Line of Defence™ is its recursive structure.
You already see this in advanced QA setups:
- Humans act
Advisers provide advice. Agents in contact centres handle calls or chats. - AI monitors humans
A platform such as Aveni Detect analyses every interaction, checks it against Consumer Duty and other criteria, and flags potential issues for QA teams. - AI monitors the monitoring
A higher level oversight agent tracks how the QA AI behaves. It looks for anomalies such as a sudden drop in flags, uneven coverage across products, or repeated misclassifications in certain scenarios.
This “watcher of the watcher” pattern matters because monitoring systems themselves can create risk if they drift. In high volume environments, even the tools that keep you safe need supervision.
Industry frameworks and Aveni’s own MLoD narrative describe this as agents monitoring agents. Machines operate and monitor in real time, while humans govern at the strategic level.
Alignment with regulators: why MLoD fits the direction of travel
Regulators are not trying to freeze AI innovation. They are raising the bar on governance and control.
- The FCA’s AI Update sets out a pro-innovation, principles-based stance, stating that many AI risks can be handled under existing rules on model governance, data quality and accountability.
- The EU AI Act introduces stricter obligations for “high-risk” AI systems, including many financial use cases such as credit scoring and risk assessment. These systems must be transparent, explainable and subject to robust human oversight.
- The FCA’s Supercharged Sandbox, created with NVIDIA, gives firms access to compute, datasets and regulatory feedback so they can test AI safely at scale before live deployment.
Underlying all of this is a simple expectation: if you deploy AI in important processes, you must be able to explain how it behaves, prove that it is under control and show that customers receive fair outcomes.
The Machine Line of Defence™ is a practical way to meet that expectation. It turns vague references to “AI governance” into concrete patterns: observable agents, recorded reasoning, continuous monitoring and clear human accountability.
How Aveni frames and delivers the Machine Line of Defence™
Aveni has been building towards this model across three core components:
- Aveni Assist and Aveni Detect
- Embed AI into advice and oversight workflows.
- Provide case-level analysis across conversations and documents, so QA and risk teams see the full context, not isolated interactions.
- The Aveni Task Registry
- Breaks financial services work into small, reusable, regulated tasks, such as checking affordability, explaining risk or evidencing consent.
- Allows firms to compose agents from proven tasks rather than opaque prompts, creating safer, more explainable agentic systems.
- The Machine Line of Defence™ itself
- Introduces agents that monitor other agents, interpret their reasoning and validate compliance against regulatory requirements.
- Provides continuous, embedded assurance across workflows, so firms can show regulators exactly how agentic systems are controlled.
Together, these elements support Aveni’s wider vision of a unified assurance platform for financial services, where human and machine oversight work together in real time.
Practical steps for Chief Risk Officers (CROs): moving towards an MLoD model
For risk leaders, the Machine Line of Defence™ is a journey, not a switch. A practical path might look like this.
1. Map where AI already sits in the first line
Identify where decisions or recommendations already rely on models, copilots and early agents, even if they are still labelled as “tools” or “pilots”. This includes:
- Advice and suitability checking.
- Fraud and transaction monitoring.
- Complaints, collections and quality assurance.
2. Instrument the key workflows
Ensure that conversations, documents, decisions and model calls are captured in a structured way. Without telemetry and decision trails, oversight will remain patchy and reactive.
3. Translate policy into machine-readable checks
Start with a small number of high value control objectives, such as:
- Consumer Duty outcomes for specific products.
- Treatment of vulnerable customers.
- High impact suitability rules.
Define how these translate into rules, thresholds and signals that agents can evaluate consistently.
4. Introduce focused oversight agents
Deploy oversight agents in a narrow, controlled context, for example:
- A monitoring agent for mortgage advice cases.
- An assurance agent that reviews transaction monitoring alerts and reasoning.
Have these agents flag specific cases for human review and gather evidence bundles automatically.
5. Keep humans clearly “above the loop”
Clarify who owns AI oversight under regimes such as SMCR. Build governance routines where risk and compliance teams:
- Review MLoD outputs.
- Approve changes to task definitions and policies.
- Engage with regulators on the firm’s AI assurance posture.
This mirrors the evolution curve in Aveni’s Machine Line of Defence™ narrative, from human-controlled operations to fully assured agentic operations, with clear stages and controls at each step.
The key takeaway for CROs
The Machine Line of Defence™ is straightforward. It treats AI agents as first-class citizens in your control framework and gives them a dedicated, digital second line.
Think of it as continuous oversight, built for machine speed. It converts telemetry, reasoning traces, and behavioural data into assurance signals that risk teams can trust. Humans remain firmly in charge of governance and judgement. Machines provide the reach and speed that modern operations demand.
For CROs, the question has shifted. AI already sits inside the first line. That ship has sailed. The urgent task now is building the Machine Line of Defence™ that keeps your agentic workforce aligned with risk appetite, regulatory duties, and customer responsibilities.
Done properly, the Machine Line of Defence™ delivers continuous, data-driven assurance at machine speed whilst meeting regulatory expectations. It gives your organisation a way to scale AI with confidence, knowing that every digital colleague has a digital control partner working alongside your human teams.
