AI Services for Security and Risk
Security is the one function where the data generating process actively adapts to your model, and that changes how everything is built.
Almost every statistical assumption behind a standard modelling approach holds less well in security, because an intelligent adversary is adjusting their behaviour in response to what you deploy.
AI services for security and risk cover alert triage, correlation and enrichment, anomaly detection against known good baselines, investigation support and case assembly, risk register and control analytics, phishing and fraud detection, and the monitoring governance these systems require.
Adversarial means the ground moves under the model
In most functions the pattern you are modelling stays roughly stable. In security an intelligent opponent observes what gets caught and changes their behaviour, which means a model that performed well last quarter may be performing well against last quarter's attacker.
- Retraining is part of the system, not maintenance. A detection capability is something you operate continuously rather than a project that completes.
- Known bad is a small and shifting set. Detecting departure from known good generalises better than learning from the attacks you have already seen.
- Evaluation is hard because the truth is unknown. You know what you caught. What you missed is by definition not in the data, which makes reported accuracy optimistic.
- Purple team exercises are the honest evaluation. Testing detection against deliberately varied techniques is closer to the truth than any historical benchmark.
- The model itself becomes a target. Adversaries probe to learn thresholds, and anything that reveals the decision boundary is information you are giving away.
Never tune sensitivity down to reduce workload
Analyst capacity is the binding constraint in every security operation, and the tempting response to alert volume is raising the threshold. That reduces the workload by not seeing things, which is the opposite of the function's purpose. The correct route is better discrimination at the same or higher sensitivity: correlation of alerts sharing a cause, suppression of known benign patterns, and enrichment so each alert carries the context an analyst needs. Volume falls and coverage does not.
Alert quality, which is the whole operational problem
The queue that cannot be cleared is the same as no queue
An alert volume exceeding analyst capacity means alerts are closed unexamined, and that is functionally equivalent to not generating them, with the added risk that the organisation believes it has coverage.
Every alert needs a reason and the evidence with it
An analyst opening an alert should see what fired, why, what else is related and what the affected asset supports. Assembling that context is where the time goes, and automating the assembly is worth more than another detection.
Explainability is required, not preferred
An alert that escalates to an incident, a regulator or a court must be explainable. An opaque score that cannot say why is not usable in that chain regardless of accuracy.
Measure conversion, not volume
Alerts that became investigations, investigations that became incidents, and incidents that were genuine. That chain tells you whether detection is working. Alert counts do not.
Risk analytics and the wider function
| Application | Fit | Note |
|---|---|---|
| Alert correlation and enrichment | Strong | The highest value work in most security operations |
| Investigation context assembly | Strong | Where analyst time actually goes |
| Anomaly detection against known good | Strong | Generalises better than learning known bad |
| Phishing and business email compromise detection | Strong | Language and behaviour together beat either alone |
| Identity and access analytics | Strong | Dormant, excessive and anomalous access is measurable |
| Risk register and control analytics | Good | Which controls actually relate to incidents you had |
| Vulnerability prioritisation | Strong | Exploitability and exposure beat severity score alone |
| Autonomous response | With care | Blast radius, rollback and a human decision point first |
Prioritise vulnerabilities by exposure, not by severity score
Severity scores are assigned without knowledge of your environment, which means a critical score on an internal system with no path from the internet may matter less than a moderate one on an exposed asset holding regulated data. Combining severity with actual exposure, exploit availability and what the asset supports produces a remediation list engineering will act on, rather than a backlog everyone has learned to ignore. The data is already in your asset and scanning records.
How an engagement runs
Alert quality first, because a queue nobody can clear makes new detection worthless.
Scope and alert analysis
Volume, conversion to investigation, and where analyst time actually goes.
Data assessment
Telemetry coverage, asset context availability, and incident history quality.
Build
Correlation and enrichment, or anomaly detection against known good baselines.
Trial
Including purple team exercises, measured on conversion and on what was missed.
Operation
Continuous retraining, with sensitivity maintained rather than tuned down.
What you receive
A queue analysts can clear, and detection that explains itself.
Alert correlation
Alerts sharing a cause grouped into one actionable item.
Alert enrichment
What fired, why, what is related and what the asset supports, assembled automatically.
Anomaly detection
Departure from known good, which generalises better than known bad.
Vulnerability prioritisation
Severity combined with exposure, exploitability and business context.
Identity and access analytics
Dormant, excessive and anomalous access surfaced for review.
Detection conversion reporting
Alerts to investigations to genuine incidents, rather than alert counts.
Is this the right starting point?
Worth being direct. There are situations in security and risk where custom AI work is the wrong spend, and those are listed rather than buried.
Worth doing if
- Alert volume exceeds analyst capacity and alerts are closed unexamined.
- Analysts spend most of an investigation assembling context manually.
- Thresholds have been raised to manage workload.
- Vulnerability remediation is prioritised by severity score alone.
- Detection performance is reported as alert counts.
Do something else if
- You want alert volume reduced by lowering sensitivity.
- Detection output must be opaque and cannot explain itself.
- Autonomous response is wanted before blast radius and rollback are defined.
- There is no appetite for continuous retraining against an adapting adversary.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What makes security different from other functions?
The data generating process is intelligent and adapts to you. In most functions the pattern being modelled is roughly stable, so a model that performed well last quarter is probably still fine. In security an opponent observes what gets caught and changes behaviour, which means yesterday's performance was against yesterday's attacker. Retraining is part of operating the system rather than maintenance, and detecting departure from known good generalises better than learning from the attacks you have already seen.
How do we reduce alert volume safely?
Correlation, suppression and enrichment, never threshold. Raising the threshold reduces workload by not seeing things, which inverts the purpose of the function. Grouping alerts that share a root cause into a single actionable item, suppressing known benign patterns and attaching the context an analyst needs reduces volume while holding sensitivity. An alert queue that cannot be cleared is functionally the same as no queue, with the added danger that the organisation believes it has coverage.
How should we evaluate detection?
With purple team exercises rather than historical benchmarks, because the truth is not in your data. You know what you caught. What you missed is by definition absent, which makes any reported accuracy optimistic in a way that is hard to quantify. Testing detection against deliberately varied techniques gives a much closer read. For reporting, use the conversion chain rather than counts: alerts to investigations, investigations to incidents, incidents that were genuine.
Why prioritise vulnerabilities differently?
Because severity scores are assigned without knowledge of your environment. A critical score on an internal system with no path from the internet may matter less than a moderate one on an exposed asset holding regulated data. Combining severity with actual exposure, exploit availability and what the asset supports produces a list engineering will act on, rather than a backlog that everyone has learned to ignore. The inputs are already in your asset inventory and scanning records.
Can we automate response?
Narrowly, and after the boundaries are defined. Automated action against a known condition with a well understood fix and a fast reversal path is reasonable and genuinely valuable outside working hours. Automated action against an ambiguous condition, or where the remediation is itself disruptive, turns a contained incident into a larger one. Define what the automation may touch, what it may never touch and how it is reversed, before it runs rather than during the first event.
Other business functions
Teams working on security and risk usually share systems, data and stakeholders with these. All twelve are listed on the Solutions page.
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Portfolio assessment, board reporting and governance, including which projects should stop.
Read more →Tell us what the problem looks like.
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