AI in Threat Detection: Why It's Not Optional

AI is used in threat detection to baseline normal behaviour, correlate signals across endpoint, network, identity, cloud and email, triage alerts by likely severity, and carry out first-line investigation under policy you set. It matters now because attackers exploit disclosed flaws within days, telemetry grows faster than analyst headcount, and human-speed triage no longer keeps pace with the adversary.

That is the short answer. The rest of this article sets out the first-principles case for it, what AI genuinely does in detection today, what it still cannot do, and how to judge a vendor claim without taking anyone's word for it.

Why AI in security is no longer a choice

The argument for AI in detection is not that AI is impressive. It is that the arithmetic of defence has changed, and a human-speed SOC is now structurally slower than the people attacking it.

Start with exploitation speed. Mandiant's time-to-exploit analysis put the average gap between disclosure and exploitation at 63 days across 2018-2019, 44 days from 2020 into early 2021, 32 days across 2021-2022, and 5 days in 2023. By M-Trends 2026, the mean had fallen to an estimated -7 days: exploitation is routinely happening before a patch exists. The same report records exploits as the most common initial infection vector for the sixth year running, at 32% of intrusions. Verizon's Data Breach Investigations Report puts the figure in the same territory, reporting that 31% of breaches now start with software vulnerabilities, ahead of stolen credentials.

If the window between "a flaw is public" and "someone is using it against you" is measured in days or less, a detection process that depends on a person reading a queue in the morning is already outside the window. That is not a criticism of the person. It is a statement about clock speed.

Attackers are also using AI themselves, though not in the way the marketing implies. Google's Threat Intelligence Group examined how state-backed actors actually used its models and concluded that current large language models are "unlikely to enable breakthrough capabilities for threat actors". What they found instead was acceleration: reconnaissance on targets, phishing content tailored to a specific organisation, code troubleshooting and translation between languages. Their summary is the useful one: "Rather than enabling disruptive change, generative AI allows threat actors to move faster and at higher volume."

Faster and at higher volume is the whole problem. You do not need attackers to invent a new technique for your SOC to fall behind. You only need them to run the existing ones more often, more cheaply, and with better English in the phishing email. Defence has to answer volume with volume, and only machines produce that.

There is a third pressure that gets less attention. Dwell time is not improving. M-Trends 2026 reports global median dwell time rising to 14 days from 11. If detection were getting faster in line with attack speed, that number would be falling.

Alert volume is a maths problem, not a morale problem

Alert fatigue is usually described as a wellbeing issue. It is really an accounting identity. Industry and academic research both name alert fatigue and burnout as significant SOC problems (Tariq et al., ACM Computing Surveys, 2025).

Alert volume scales with the amount of telemetry you collect. Telemetry grows with every new SaaS application, cloud account, identity provider and endpoint. Analyst headcount does not grow with any of those things; it grows with budget, and budget grows slowly. So the ratio of alerts per analyst rises every year by construction, whatever anyone's attitude to the work.

Two bad responses follow. The first is to collect less, which is how organisations end up blind in exactly the places attackers prefer. The second is to raise detection thresholds until the queue is manageable, which trades a visible workload problem for an invisible miss rate.

The third response is to change what handles the first pass. In one Fortune 500 environment SenseOn protects, 33.4B events are analysed every month. Across more than 30 million investigated cases over a rolling twelve-month window, confirmed true positives run at 0.68%. Roughly 99 in every 100 things that look worth a look are not. Interviews with analysts across seven SOCs found the same picture: the tools produce high false-positive rates that need manual validation, and validating alarms is tedious work that leads to burnout and eventually desensitisation (Alahmadi et al., USENIX Security, 2022). No headcount plan closes that gap. A first pass that runs at machine speed, and shows its working, does.

The measure that matters is not how many alerts you closed. It is how much of the queue a human never had to touch, and whether you can prove the machine was right about the rest. SenseOn resolves 92.5% of cases under human governance (cases resolved without human escalation ÷ cases opened, rolling 30 days, all customer environments; working on proof points) and reports a detect-and-respond time of <20 min. Those are our numbers, on our platform, and you should ask any vendor including us for the equivalent, defined the same way. There is more on cutting queue volume without cutting visibility in how to reduce alert fatigue.

Per-GB economics punish the thing that makes detection work

If you are evaluating an AI SIEM alternative, start with the billing unit, not the model. Most SIEM pricing charges by volume ingested. That creates a direct conflict between the finance owner and the detection engineer: every additional log source that improves coverage also increases the bill, so the cheapest security posture is the blindest one. Teams respond by filtering at the source, sampling, or shortening retention, and then discover during an investigation that the month they needed is gone.

AI triage inverts the relationship, but only if the commercial model lets it. If the machine handles the first pass, more data means more context for correlation and a better-informed verdict, not more human hours. That only translates into a lower bill when ingest does not drive the whole invoice. SenseOn prices outcomes through credits, with data volume as one meter, and removes ~40% of data at the edge before the pipeline starts, so the volume that does travel is the volume worth analysing.

The point is not the pricing model of any one vendor. It is that you should check whether the tool's economics reward visibility or punish it, because that decides how much of your estate you can afford to watch. SIEM alternatives covers the trade-offs in more detail.

What AI genuinely does in detection today

Strip out the marketing and four capabilities are real and in production across the industry. Together they are what "AI-powered alert tuning and correlation" means when a vendor says it.

Baselining and anomaly detection. Unsupervised models build a statistical picture of normal behaviour for a user, host, service account or network flow, then flag deviation. This is mature technology, and it is the core mechanism behind AI insider threat monitoring tools: no rule can describe "this person is behaving unlike themselves", so behaviour, not rules, has to carry the detection (Yuan and Wu, Computers & Security, 2021 review why rule-based and hand-engineered approaches struggle to capture those behavioural differences). It is also the noisiest capability on its own, because unusual and malicious are not the same thing.

Cross-domain correlation. Individually weak signals from endpoint, network, identity, cloud and email become strong when joined. A failed authentication is noise. A failed authentication, followed by a successful one from a new location, followed by a mailbox rule creation and an outbound transfer, is a case. Doing that joining by hand across five consoles is the work that eats an analyst's day.

Triage and prioritisation. Models scored on historical outcomes can rank what deserves human attention first. This is where the volume problem is actually solved, and where independent evidence matters most: in AV-Comparatives Real-World Protection testing, SenseOn recorded a 95.9% real-world protection rate with zero false alarms in the 2024 test (full results). Detection rate on its own is easy to buy with aggressive heuristics; the pairing is the hard part.

Investigation and evidenced containment under policy. The newest capability, and the one that separates vendors. An agent gathers the supporting evidence, forms a verdict, and where policy allows, takes a contained action such as isolating a host or disabling a session, recording what it did and why. Will AI reduce SOC work? sets out which parts of the job this genuinely removes.

What AI still cannot do

Three limits are structural, not a matter of waiting for a better model.

Intent and business context. A model can tell you that a finance user is pulling unusual volumes from a database. It cannot know that year-end reporting started on Monday. Context that lives in people's heads, or in systems the platform does not see, has to be supplied by a person or encoded as policy.

Truly novel technique with no behavioural footprint. Models generalise from what they have seen. A technique that produces no anomalous behaviour anywhere in your telemetry is invisible to any detector, statistical or human. In practice this is rarer than vendors admit and more common than buyers hope, which is why MITRE ATT&CK coverage mapping is still worth doing.

Unexplained decisions. If a platform cannot show why it reached a verdict, you cannot review its judgement, audit it, or defend it to a regulator. A black-box call is not a decision you own; it is a decision you inherited. That limit is the reason governance is not an optional extra.

Governance is what makes AI in security safe to adopt

The uncomfortable question about autonomous detection is not "is it accurate?" It is "what happens the day it is wrong, and can you show what it did?"

Four conditions make the answer manageable, and they should be contractual, not aspirational:

• Every action evidenced. A complete, ordered, timestamped record of what the AI did and what a human did, attributable to each. SenseOn's Decision Trace is append-only and covers 100% of platform actions by design, not by sampling.
• Every action reversible. A rollback that has been exercised, not one that exists in the architecture diagram.
• Every action inside a policy you set. The boundary of autonomy is your decision, per action type, not a vendor default.
• A human owns the exceptions. Escalation against a stated threshold, with the threshold written down and reviewed.

If you are being asked to approve wider autonomy for any AI workflow, the seven boardroom questions are the test to run first: they check whether you could reconstruct, contain and undo what an agent did, using evidence you hold today.

Human-only, AI-assisted and AI-governed SOCs compared

Most organisations sit in the middle column, carrying the cost of the tooling and the analysts at once.

How to evaluate an AI security tool

Six questions, in the order that saves you the most time.

1. What exactly is the AI doing? Ask which of the four capabilities above the product performs. "AI-powered" applied to a static rule engine is a marketing claim, not a capability.
2. What is the false positive evidence, from an independent test? Vendor-run benchmarks tell you what the vendor optimised. Ask for a named third-party test, the date, and the configuration used.
3. Can it explain a verdict to a non-specialist? Ask to see the evidence behind one real detection during the evaluation, not a slide about explainability.
4. What can it do without asking, and who set that boundary? Get the default autonomy policy in writing, and confirm you can narrow it per action type.
5. Can you reconstruct and undo an action six weeks later? Ask for the record format and whether it is append-only. Then ask whether a rollback has ever actually been run.
6. What is the billed unit? If it is ingest volume, model the cost of the visibility you actually want, not the visibility you have today.

Run those six against every vendor including the incumbent. The comparison in six EDR solutions compared applies the same framework across the endpoint market, and the best insider threat detection tools does it for insider risk, where baselining does most of the work.

Working with the EDR and SIEM you already run

The honest answer to "do I have to replace CrowdStrike, SentinelOne, Splunk or Elastic?" is no, and you should be suspicious of anyone whose answer depends on you doing so.

An AI SOC layer takes signals from the tools you already run and does the correlation, triage and investigation across them. Your EDR keeps doing endpoint prevention and response. Your SIEM keeps its compliance retention and its saved searches. What changes is who does the first pass on the output, and where the joined-up case record lives.

Two caveats worth stating plainly. First, an AI layer can only correlate what it can see, so the value depends on which telemetry you actually forward, and a partial feed produces partial cases. Second, running an AI SOC layer alongside a per-GB SIEM does not by itself reduce the SIEM bill; it reduces analyst load. Consolidation is a separate decision, and a later one. Make the triage change first, measure it, then decide what you still need underneath.

For the same reason, no data migration is required to start. You are adding a consumer of telemetry, not moving the store. That distinction is what makes an evaluation cheap enough to be honest.

Where to start

Pick your noisiest detection domain, usually identity or email. Measure three things for a fortnight: alerts raised, alerts a human opened, and how many turned out to be real. That gives you a baseline true-positive density for your own environment. Then run one AI tool against the same domain and measure the same three numbers, plus a fourth: for each case it closed on its own, could you reconstruct why?

If you cannot answer the fourth question, the accuracy of the first three does not matter yet.

Frequently asked questions

Does EDR use AI?

Yes. Modern EDR uses machine learning in two places: pre-execution classification of files and behavioural analysis of running processes. Most platforms also apply models to alert prioritisation. What varies is whether the AI only scores endpoint events, or correlates them with network, identity and cloud signals to build a case.

Can an AI SOC work with CrowdStrike or SentinelOne without replacing them?

Yes. An AI SOC layer consumes endpoint telemetry and alerts from your existing EDR and correlates them with network, identity, cloud and email signals. The EDR keeps doing prevention, isolation and response on the endpoint. What changes is who performs the first-pass triage and where the joined-up case record and audit trail live.

Does an AI SOC platform work with an existing Splunk or Elastic SIEM?

Yes, and no data migration is needed. The AI layer subscribes to the telemetry it needs; your SIEM keeps its retention, compliance reporting and saved searches. Be clear about the outcome: this reduces analyst triage load first. It only reduces your ingest bill if you later retire log sources, which is a separate decision.

How do AI security tools show ROI?

Measure four things before and after: alerts a human had to open, mean time to detect and respond, true positives as a share of investigated cases, and ingest spend for the visibility you want. SenseOn reports 92.5% of cases resolved under governance (cases resolved without human escalation ÷ cases opened, rolling 30 days, all customer environments; working on proof points), a detect-and-respond time of <20 min, and pricing in which data volume is one meter, not the whole bill. Ask any vendor for those four, defined identically.

What can AI not do in threat detection?

Three things. It cannot supply business context it was never given, such as knowing that year-end processing explains an anomaly. It cannot detect a technique that leaves no behavioural trace anywhere in your telemetry. And it cannot make a decision you can defend if it cannot show its reasoning, which is why evidenced, reversible action inside your policy matters more than raw accuracy.

What is an AI SOC agent?

An AI SOC agent is software that performs an analyst task end to end rather than scoring an alert: gathering evidence, forming a verdict, and where policy allows, taking a contained action such as isolating a host. The useful test is not autonomy but accountability, whether every action it takes is recorded, attributable, reversible and inside a boundary you set.

Sources

• Shahroz Tariq, Mohan Baruwal Chhetri, Surya Nepal, Cécile Paris, "Alert Fatigue in Security Operations Centres: Research Challenges and Opportunities", ACM Computing Surveys, 2025, https://doi.org/10.1145/3723158 (open access)
• Bushra A. Alahmadi, Louise Axon, Ivan Martinovic, "99% False Positives: A Qualitative Study of SOC Analysts' Perspectives on Security Alarms", USENIX Security Symposium, 2022, https://www.usenix.org/conference/usenixsecurity22/presentation/alahmadi (open access)
• Shuhan Yuan, Xintao Wu, "Deep learning for insider threat detection: Review, challenges and opportunities", Computers & Security, 2021, https://doi.org/10.1016/j.cose.2021.102221 (open access: https://arxiv.org/abs/2005.12433)
• Google Cloud, M-Trends 2026 and time-to-exploit analysis (linked in the text)
• Verizon, Data Breach Investigations Report (linked in the text)

Related reading:

• Provider logs can't prove what your AI agent did
• Will AI reduce SOC work?
• Does AI pentesting work?
• Explore the platform

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