Glyph Guard
Proprietary security infrastructure, built from the ground up.
The platform behind every NXT assessment. Purpose-built to inspect AI agent behavior in real time and produce the evidence our assessments are based on.
How It Works
Not an AI watching your AI.
Entirely deterministic: written rules and statistical analysis that produce the same verdict every time. There is no model in the loop to hallucinate, drift, or be talked out of a decision.
inspects every request, blocks threats in milliseconds
scans every response, stops data leakage
inspects every request, blocks threats in milliseconds
Modelscans every response, stops data leakage
Your UsersBefore the model ever sees it, Glyph Guard inspects it. Anything malicious or outside your policy is blocked in milliseconds, and the model never runs.
Inspection
<10ms
Deterministic
No model sits in the detection path. The same input produces the same verdict every time, with nothing to hallucinate or be talked out of.
Powers every assessment
Every AI security audit we deliver is run through this platform. The findings, evidence, and compliance mappings in our reports come directly from what the platform surfaces.
Real engineering
Purpose-built detection and statistical risk scoring behind every decision. Not a prompt, not a wrapper, not a model.
What It Covers
If an AI agent can touch real data or real systems, it can be attacked. These are the deployment types our platform is built to assess.
- -Block manipulation attempts before the bot ever reads them
- -Stop an agent from revealing one customer's data to another
- -Detect pressure that escalates gradually across a conversation
- -Keep every conversation inside the policies you define
- -Block manipulation attempts before the bot ever reads them
- -Stop an agent from revealing one customer's data to another
- -Detect pressure that escalates gradually across a conversation
- -Keep every conversation inside the policies you define
Tested
Select a model to see how it held up.
Seven models, from a commercial API down to small models on edge hardware, each put through the same adversarial suite.
0
Payloads tested
0.0%
Stopped with Glyph Guard
0.0%
Model's own defense
+0.0%
Guard uplift
All models · 7 models · API + edge · 1,632 payloads · Phase 1 + Session 49, April 2026
Each model was run twice against the same payloads: once with Glyph Guard, once without. Detection includes the quantitative risk layer, which holds flagged responses before delivery; deterministic scanners alone average 96.0%.
Research
Published research and adversarial evaluations from our security research program. Controlled testing, documented methodology, and measurable results.
AI Security Audits
See what an assessment powered by this platform looks like.
Independent adversarial testing for AI systems in production. The same rigor applied to the models your business depends on.
Explore AI security audits →Contact
For partnership inquiries and research collaboration.