Cyber threats are growing in volume and sophistication. We help organisations use AI to detect threats faster and build secure applications, data platforms and AI systems from the ground up.
AI in cybersecurity uses machine learning and language models to detect unusual activity, prioritise alerts, spot phishing and fraud, analyse threat intelligence and help secure software and cloud environments, so security teams respond faster to real threats with the people they have.
We work with log and security data from SIEM, EDR, firewalls, identity providers and cloud platforms, using streaming pipelines and analytics platforms such as Azure Data Explorer and Microsoft Sentinel. Detection models are built in Python and tuned on your environment to keep false positives low. Generative AI is used to summarise incidents and threat reports, with analysts reviewing its output. AI systems we build follow guidance such as the OWASP Top 10 for LLM applications.
Security clients use our data integration, AI development and AI integration services. You can also hire DevOps engineers or data engineers to strengthen your security data platform.
Is your team overwhelmed by alerts? Tell us about your security stack, and we will suggest where AI can reduce the load. Start your AI journey.
Answers to common questions about AI for security operations and secure AI.
No. AI helps analysts by filtering noise, prioritising alerts and summarising incidents, but people still investigate, decide and respond to threats.
Models learn the normal behaviour of your users, devices and systems, so they flag genuinely unusual activity. Analyst feedback on each alert is used to improve accuracy over time.
We apply input and output filtering, protection against prompt injection, least-privilege access to data and tools, logging and red-team testing, following guidance such as the OWASP Top 10 for LLM applications.
Yes. We integrate with SIEM platforms such as Microsoft Sentinel and Splunk, as well as ticketing tools, so AI results appear where analysts already work.
Cost depends on data volumes, the number of sources and the use case. Most clients start with a fixed-price assessment and pilot on one alert type or data source.
A pilot on one data source, such as identity or network logs, typically takes six to ten weeks, including tuning on historical incidents.
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