IN-MODEL DEFENCE FOR LLM AND AGENTIC AI
Beat Prompt Injections
CyberProfiler is the only solution that acts on a 'malicious intent' signature in activation space - not inputs. Now CISOs have the upper hand. Up to 99%+ effective ↓
Start out of the box. Tune to your data and models when ready.
TIER A: OUT OF THE BOX
90%+ accuracy
Under 250ms latency
Trained on 24 classes of prompt injection. Runs as an input guardrail against a reference model.
Available now in early access. Try it above.
TIER B: YOUR DATA
95%+ accuracy
Under 250ms latency
A bespoke Profiler tuned on your traffic patterns and edge cases, run as an input guardrail.
TIER C: YOUR DATA AND MODEL
99%+ accuracy
Under 75ms latency
Instrumented directly into your model's reasoning path, trained on your production model and data.
Figures are targets from validation on labelled test sets and a customer proof of concept, and depend on your model and traffic. The demo and early access use Tier A.
Proven, patent-pending technology
Spring '26. The National Cyber Security Centre (NCSC is part of GCHQ), and Cisco were partners for a project evaluating TIKOS® CyberProfiler for an agentic workflow in a critical national infrastructure (CNI) setting.
National Cyber Security Centre, Cisco Systems Inc.
Summer '26. The IEEE Aerospace group awarded TIKOS® a research award to apply CyberProfilers to a federated learning (FL) system coordinating remote drone swarms.
Institute of Electrical & Electronics Engineers
Autumn '26. TIKOS® presented a talk and poster 'Reasoning Not Rationalising' at the UK’s only annual, government-backed conference dedicated to the intersection of Artificial Intelligence and Cybersecurity
AMLUCS: Applied Machine Learning for Cyber Security
Ongoing engagement since Spring '26. The Laboratory for AI Security Research (LASR) brings together academia, industry, and government to advance UK leadership in AI security research.
LASR: Laboratory For AI Security Research
Stop patching. Block intent.
CyberProfiler is part of the TIKOS® AI Assurance Platform.
