AI for Cybersecurity Companies
Security is the one domain where your data distribution shifts because someone is deliberately trying to make it shift, which invalidates most assumptions that hold everywhere else.
Every security team has too many alerts and not enough analysts, which makes triage the obvious application. What makes it hard is that reducing alert volume without evidence that you preserved detection is not an improvement — it is a risk you cannot see.
AI for cybersecurity covers alert triage and false positive reduction with recall evidence, investigation context assembly, detection engineering support, threat intelligence processing, and behavioural anomaly detection — with autonomous response treated as a distinct and higher-risk category.
Reduction without recall evidence is not a result
Any model can cut alert volume by eighty per cent. The question a security leader cannot avoid is what went with it, because the failure mode is a missed intrusion and the consequence attaches to them personally.
- Instrument recall from the start. Sample suppressed alerts and investigate them conventionally; back-test against known historical incidents.
- Rank rather than suppress, initially. Ordering the queue captures most of the analyst time saving with far less exposure, and it is a much easier internal sell.
- Analyst dispositions are the training signal. What analysts concluded and why is the label that makes triage models work, and most teams capture the disposition and discard the reasoning.
- Explanations must be case-file usable. An analyst has to write up why an alert was escalated or closed, and a confidence score does not help them do that.
- Report recall alongside reduction, permanently. Not as a launch study but as a standing metric, because both drift.
Rank first, suppress later, and only with evidence
The version of this that gets deployed and stays deployed is queue ranking with enrichment: the analyst sees everything, in a better order, with context already assembled. Auto-closure is a conversation a security leader has to have with their auditors and their board, and most will not have it on a vendor's aggregate accuracy figure. Build the ranking product, accumulate recall evidence over months, and let suppression be something the organisation opts into with its own data behind it.
Adversarial drift is different from ordinary drift
In every other domain, model drift happens because the world changes. In security it happens because someone is probing your defences to find out what they can get past, which changes both the cadence and the character of the problem.
Attackers test against your controls
Detection that works becomes detection worth evading, and evasion is developed deliberately and quickly. Retraining cadence and drift sensitivity both need to be higher than they would be for a fraud or a maintenance model.
Novel attacks are absent from training data by definition
A supervised model trained on known attack patterns detects known attack patterns. It is a complement to behavioural and anomaly-based detection rather than a replacement, and presenting it otherwise is where security AI oversells.
The model itself is attack surface
Prompt injection into anything processing untrusted input, poisoning of training data, and adversarial examples designed to evade classification are real in this domain rather than theoretical. See LLM security and AI red teaming.
Measure detection on matured incidents
Intrusions are discovered late, so recent-period detection rates always flatter you. Report on periods old enough for the incidents to have surfaced.
Where autonomous response sits, and why we hold it back
| Capability | Our position | Reasoning |
|---|---|---|
| Alert ranking and enrichment | Build | Clear value, low risk, easy to evidence |
| Investigation context assembly | Build | Saves real analyst time on every case |
| Detection rule suggestion | Build | Human reviews and deploys; a good fit |
| Threat intelligence processing | Build | High volume, structured, immediately useful |
| Phishing and BEC classification | Build | Strong, with a well-defined error trade-off |
| Automated containment on high confidence | Careful | Blast radius must be bounded and reversible |
| Autonomous response across the estate | Decline for most | Correlated failure at exactly the wrong moment |
Why we are cautious about autonomous response
Not because it cannot work, but because its failure modes are correlated with the moments you least want a surprise. An automated containment action during a genuine incident, based on a model an adversary may have been probing for weeks, can take down production faster than the attack would have. Where clients want automation, we build it with a bounded blast radius, a reversible action, and a human authorisation for anything wide — and we say plainly that this is the constraint we will not negotiate away.
How an engagement runs
Recall measurement is instrumented before any reduction claim is made.
Scope and recall design
How preserved detection will be evidenced, and what the organisation would need to see before suppressing anything.
Data and disposition foundation
Alert history, analyst dispositions and reasoning structured into something that supports supervised learning.
Build
Queue ranking with enrichment and case-file usable explanations, with recall instrumented from the first day.
Shadow running
Against current triage, measuring analyst time saved and recall on sampled suppressions.
Operation
Higher retraining cadence for adversarial drift, recall reported as a standing metric alongside reduction.
What you receive
Analysts spending time on the alerts that matter, with evidence you did not lose detection.
Recall instrumentation
Sampled suppression investigation and back-testing against known incidents.
Alert ranking and enrichment
Everything visible, better ordered, with context assembled before the analyst opens it.
Case-file usable explanations
Reasons an analyst can write into a disposition, not a confidence score.
Disposition capture
Analyst reasoning retained as the training signal most teams discard.
Adversarial drift monitoring
Higher cadence and sensitivity than ordinary drift, because someone is probing deliberately.
Bounded automation
Where automation is in scope: limited blast radius, reversible actions, human authorisation for anything wide.
Is this the right starting point?
Worth being direct. There are situations in cybersecurity where custom AI work is the wrong spend, and those are listed rather than buried.
Worth doing if
- Alert volume exceeds analyst capacity and triage is first-in-first-out.
- You claim or need alert reduction and cannot currently evidence preserved recall.
- Analyst dispositions are captured without the reasoning behind them.
- Investigation time is spent assembling context rather than assessing it.
- You are a vendor and customers' security teams need validation evidence from you.
Do something else if
- You want auto-closure without a recall story. Your auditors will ask and so will your board.
- You want autonomous response across the estate. We decline that for most organisations.
- Alert and disposition history is unavailable, so there is nothing to learn from.
- The real problem is coverage gaps, which triage will not address.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
How do we know alert reduction did not lose detection?
By instrumenting it rather than asserting it. Sample suppressed alerts and investigate them conventionally, back-test against known historical incidents, and report recall alongside reduction as a standing metric rather than a launch study. Any model can cut volume; the vendors and teams that keep their deployments are the ones who can show what went with it.
Should we automate response?
Cautiously and narrowly, if at all. The failure modes of autonomous response are correlated with the moments you least want a surprise — an automated containment during a genuine incident, driven by a model an adversary may have been probing, can take down production faster than the attack. Where we build automation it has a bounded blast radius, reversible actions and human authorisation for anything wide, and that constraint is not negotiable.
Why does our detection model degrade faster than expected?
Because the drift is adversarial rather than natural. Everywhere else, models degrade because the world changes; in security they degrade because someone is deliberately probing to find what gets through, and evasion is developed quickly once a detection works. Retraining cadence and drift sensitivity both need to be higher here, and detection rates should be measured on matured periods because intrusions surface late.
Can AI detect novel attacks?
A supervised model trained on known patterns detects known patterns — that is a definitional limit rather than a shortcoming. Behavioural and anomaly-based approaches complement it by flagging deviation from normal without needing attack examples, at the cost of a higher false positive rate. Presenting supervised detection as novel-attack capability is where security AI most often oversells, and it is a claim worth pushing back on.
What do our customers' security teams need from us as a vendor?
Documentation their own validation processes can accept: what the model does, on what data, with what measured performance on populations resembling theirs, how it explains its decisions, what evidence supports any reduction claim, and how you handle version changes. Aggregate accuracy across your customer base does not answer any of those, and increasingly it does not get through procurement either.
Related verticals
Organisations in cybersecurity usually share data, buyers or regulators with these. All fourteen are listed on the Technology & Telecom page.
AI for Managed Service Providers
Ticket triage, alert noise reduction and per-client margin — with client segregation enforced architecturally.
Read more →AI for Telecommunications Operators
Network optimisation, fault root cause, churn and field operations — at the scale telecoms actually runs.
Read more →AI for SaaS and Software Companies
In-product AI features with evaluation and unit economics — plus the provider question settled per product.
Read more →Tell us what the problem looks like.
Thirty minutes, no charge, no deck. We will tell you whether this is an AI problem, a data problem, or a process problem — and we will say when the honest answer is to buy something rather than build it.