An AI firewall for your LLM apps and agents.
AI opens a new attack surface: prompt injection, data leakage, jailbreaks, and agents taking unsafe actions. We put guardrails, red-teaming, and governance around your AI so it holds up in production, and in front of your auditors.
What an AI firewall covers
Six layers, from the prompt to the audit log
Configured to your app, your data, and your risk. Retrofit onto AI you already run, or build in from the start.
AI firewall and runtime guardrails
A policy layer at the LLM boundary that inspects every prompt and response before it reaches your model or your users.
- Prompt-injection and jailbreak detection on inbound requests
- PII and secret redaction, plus output validation against your policies
- Allow and deny rules per route, tenant, and user role
Red-teaming and adversarial testing
We attack your AI the way a real adversary would, then turn what we find into tests that run on every change.
- Prompt-injection, jailbreak, and data-exfiltration test suites
- Tool and agent abuse scenarios, mapped to the OWASP LLM Top 10
- Regression gates so a fix stays fixed across model and prompt updates
Secure RAG and agents
Retrieval and agents that respect your permission model, so AI never surfaces a document or takes an action it should not.
- Permission-aware retrieval: access checks enforced at query time
- Tool-use guardrails and human-in-the-loop for high-risk actions
- Scoped agent authority with a kill switch and full audit trail
AI governance and audit
The controls and evidence an enterprise needs to put AI in front of auditors, security teams, and regulators.
- Immutable audit logs of every prompt, retrieval, and action
- Model, prompt, and policy versioning with change history
- Evidence packs aligned to SOC2 and GDPR expectations
Monitoring, abuse detection, and response
Live visibility into how your AI is used and misused, with alerts and a runbook for when something goes wrong.
- Anomaly and misuse detection, with cost and abuse alerting
- Drift and quality monitoring on live traffic
- Incident runbooks and a kill switch you can actually pull
Evaluation and release gates
The same evaluation discipline behind our production AI work, wired in as a gate so unsafe changes never ship.
- Accuracy and hallucination thresholds as blocking release gates
- Confidence scoring with routing to human review
- Continuous offline and online evaluation
Red team
We attack it before an attacker does.
A guardrail you have not tested is a guess. We run prompt-injection, jailbreak, and data-exfiltration attacks against your AI, mapped to the OWASP LLM Top 10, then hand you proof of what got through and a fix for each. Every finding becomes a regression test, so the same attack does not work twice.
Where AI breaks
The failures we are hired to prevent
Concrete ways LLM apps and agents go wrong, and how the firewall stops each one.
A chatbot follows injected instructions
A user hides an instruction in a message or a document, and your assistant leaks data or ignores its rules. The firewall catches the injection before the model acts on it.
An agent takes an unsafe action
An autonomous agent is one bad tool call away from a refund, a delete, or an email it should never send. We scope its authority and put a human in the loop on high-risk steps.
RAG surfaces documents it should not
Retrieval pulls a file the user is not cleared to see. We enforce your access model at query time, so the assistant is blind to anything the user cannot open.
Auditors ask how the AI is controlled
Security and compliance want evidence, not assurances. We give you audit logs, versioned policies, and an evidence pack that answers the questionnaire.
Jailbreaks bypass your safety prompt
A clever prompt talks your model past its own guardrails. We red-team for it, then add detection and regression tests so the same trick does not work twice.
Proof
The guardrails behind our production AI
AI security is a newer packaged service for us, but the discipline is not. These are real engagements where trust, access control, and human oversight were the whole point.
The risk: AI drafts for regulated audit work had to be trustworthy enough for a partner to stake a sign-off on, across engagements in 6 countries.
What we did: We built a citation-first architecture where every claim links to a source, routed low-confidence output to human review, wrote everything to an immutable audit trail, and gated releases behind a 200-plus case evaluation suite.
The risk: An AI knowledge assistant across 12 internal systems and 15,000 users could surface documents a person was never cleared to see.
What we did: We put access control first: the model is filtered against each user's permissions at query time, validated by HUL's security team before a single document was indexed, with a full audit trail across 8 countries.
The risk: An autonomous reconciliation agent handling financial discrepancies could not be trusted to act without oversight, and compliance required human sign-off.
What we did: We kept a human in the loop on every resolution, built a 500-plus case evaluation suite as a release gate, and tracked escalation rate as a weekly health SLA so drift surfaced before it became a problem.
How we deliver
From threat model to a firewall in production
Threat model and assessment
We map your AI attack surface, data flows, and blast radius, then test how your current defenses hold up.
- AI threat model and data-flow map
- Findings from an initial red-team pass
- Prioritized risk register
Design the guardrails
We design the firewall rules, permission model, human-in-the-loop points, and the policies your AI must enforce.
- Guardrail and policy design
- Permission and access model
- Human-in-the-loop decision map
Red-team and harden
We attack it, close the gaps, and turn every finding into a regression test that runs on future changes.
- Adversarial test suite
- Remediations for confirmed findings
- Release gates wired into CI
Deploy and govern
The firewall goes live with monitoring, audit logging, and runbooks, then we hand over to your team.
- Runtime firewall and monitoring live
- Audit logging and evidence pack
- Incident runbooks and handover
Ways to engage
Start with an assessment, or build the firewall. Priced up front.
Security assessment and red-team
Scoped, fixed fee
2 to 4 weeks
We threat-model your AI, red-team it, and hand you a prioritized report of what to fix and how.
Guardrails and firewall build
$25k to $90k typical
3 to 8 weeks
We design and build the runtime firewall, secure retrieval and agents, and wire in evaluation gates.
Ongoing monitoring and assurance
Scoped monthly
Rolling, minimum 3 months
We run monitoring, keep the red-team suite current, and provide audit evidence as your AI evolves.
Frequently asked
Questions security and engineering teams ask about AI security
Ship AI you can defend.
Tell us what your AI does, and we will threat-model it, red-team it, and put a firewall around it.
Ready to build
something real?
Every week without the right software is a week your team works harder than they need to. Tell us what you're building. We'll tell you how fast we can ship it.
- 01
Response within 4 hours, not days
We read every message personally.
- 02
Free 30-min scoping call, zero sales pitch
We will tell you if we are the right fit, and if not, who is.
- 03
Clear scope and timeline before any commitment
No surprises. You know exactly what you are getting.
The team behind your next build
