Spam cut by 20% overnight.
Threat detection up 21%.
Fine-tuned AI models, trained on millions of data points and retrained continuously, catch more real threats while eliminating the false positives that used to erode trust.
from rollout
detected
behind every model
at launch
Client
A leading cybersecurity business, protecting enterprise and consumer users against spam and malicious messaging at scale.
Goal
The company set out to reduce spam volume and sharpen malicious threat identification, without disrupting legitimate communications.
Real threats and false alarms looked the same.
Attackers kept changing delivery patterns, obfuscating content, and rotating infrastructure. Historical rules and static filters couldn’t keep up, and every missed nuance meant either a threat got through or a legitimate message got blocked.
The business needed a system that could tell real spam and malicious activity apart from harmless variations in ordinary traffic, at a scale of billions of events, without dragging down user experience or operational headcount.
Three constraints were non-negotiable:
- Distinguishing malicious intent from benign, borderline traffic
- Eliminating false positives that block legitimate messages
- Scaling detection to billions of events without added friction
Models trained to read intent, not just patterns.
The team built a fine-tuned AI identification pipeline on machine learning models trained on millions of labelled data points, drawn from message metadata, content signals, sender reputation, user interaction patterns, and third-party threat intelligence. Crucially, the pipeline captured data on an ongoing basis, so models kept absorbing new attack patterns rather than working off historical snapshots.
The models were trained to read intent, not just patterns, and to keep learning after release, not only before it.
Iterative fine-tuning combined supervised learning with human review and semi-supervised techniques to surface edge cases. Feature engineering was built around intent signals rather than surface heuristics, and every evaluation weighed detection rate against precision, because precision was where the old approach was failing.
Before release, the models went through extensive A/B testing and validation against live traffic. By the time of rollout, false positives were all but eliminated in every context tested.
More threats caught. Fewer false alarms. Less spam.
Stronger defence against evolving adversaries, with far less collateral damage to legitimate communications. Together, the results turned the product into a genuinely leading cybersecurity offering, and proved the case for continuous, data-driven fine-tuning over static rules.
* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.
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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.