Runtime Security for Agentic AI
LLM Drift Detection on Every Response Your AI Sends
PromptHalo watches your AI input and output streams in real time, spots drift session over session, and can block risky actions before they reach users.
- Tracks output shifts session over session, not one bad reply
- Inline decisions in under 100ms on every inference and tool call
- Deploys in under a day, no retraining and no code rewrite
ML-based detection: over 95% catch rate at under 5% false positives.
Get a quote for LLM drift detection
Tell us about your AI stack and what you need to watch. Our team will follow up with next steps.
Why teams use PromptHalo for drift detection
See drift before users do
Detection tracks how outputs change across sessions, so slow shifts get caught early instead of after a complaint.
Fewer false alarms
ML-based detection catches over 95% of issues at under 5% false positives, versus roughly 35% catch and 15-20% false positives for rule-based checks.
Detection plus enforcement
The same inline layer can allow, restrict, challenge, deny or monitor each action, so a risky response can be stopped, not just logged.
Evidence you can replay
Every decision is written to a tamper-evident, append-only log with the reason, agent identity, session and timestamp.
No changes to your models
PromptHalo works without access to your proprietary models. No retraining, no code rewrite, and it is model and vendor agnostic.
Built for regulated work
Audit logs map to the OWASP LLM Top 10, NIST AI RMF and the EU AI Act, which suits fintech, payments and other regulated teams.
What LLM drift detection covers
Behavioral drift is the quiet change in AI output that builds up over time. One answer looks fine, but behavior shifts session over session until trust and compliance are at risk. PromptHalo's Behavioral Drift Detection uses per-tenant session and memory state from the context store to recognize when outputs move away from expected behavior.
Drift detection is part of PromptHalo's Runtime Security solution, so the same inline layer that spots the shift can also act on it. Detection runs in milliseconds on the wire, and each action gets a decision in under 100ms.
- Tracks gradual and subtle output shifts across sessions, not just single responses
- Uses per-tenant session and memory state to compare behavior over time
- Runs on real-time monitoring of AI application input and output streams
- Feeds the same pipeline that enforces allow, restrict, challenge, deny or monitor
- Pairs with prompt injection, jailbreak, RAG poisoning and data leakage protection
- Blocks out-of-scope tool and API calls by agents before they execute
- Writes decision-level, append-only, tamper-evident audit logs for review and export
- Deploys by API gateway, agent mode with orchestration platforms, or inline middleware SDK
What happens next
Share your setup
Send the form with your AI applications, agents and what drift or risk you need to watch.
Talk through fit
Our team reviews your stack and shows how detection and inline enforcement would apply to it.
Connect and enforce
Deploy through API gateway, agent mode or inline middleware, with no model retraining and no code rewrite.
Frequently asked questions
How is this different from logging or evaluation runs?
Evaluations look at samples after the fact. PromptHalo inspects live input and output streams and compares behavior across sessions, then makes an inline decision on each action in under 100ms.
Do you need access to our model?
No. PromptHalo works without access to the underlying model. There is no model retraining and no code rewrite, and it is model and vendor agnostic.
How long does deployment take?
The Runtime Security solution deploys in under a day. You can connect through API gateway integration, agent mode with orchestration platforms and agent frameworks, or an inline middleware SDK.
What does it cost?
Pricing is not published. Send your AI stack details and use case through the form and we will scope it with you.
Can we set our own rules for what counts as a problem?
Yes. The Policy Enforcement Engine lets you define custom rules to flag, log or block AI responses in real time, applied per action with the outcome recorded.
Do you support compliance reporting?
Every decision is captured with its reason, the acting agent or passport identity, session and tenant context and a timestamp in an append-only, tamper-evident log mapped to the OWASP LLM Top 10, NIST AI RMF and the EU AI Act.
Who do you work with?
PromptHalo serves US enterprises deploying AI and agentic AI, with a focus on regulated financial services, fintech and payments.
Catch LLM drift before it becomes an incident
Tell us about your AI applications and agents. We will show how PromptHalo detects drift across sessions and enforces your policy on every action.
- Tracks output shifts session over session, not one bad reply
- Inline decisions in under 100ms on every inference and tool call
- Deploys in under a day, no retraining and no code rewrite