AI threat protection that blocks malicious traffic,
not business growth
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When blocking fails, so does AI’s promise
Legacy blocking tools misclassify benign AI traffic leading to failed sessions, abandoned workflows, and frustrated users at scale. Instead of delighting customers with AI-driven experiences, enterprises lose them at checkout or onboarding.
Every time an AI-powered transaction gets blocked by mistake, money is lost. Legacy WAFs introduce friction that drops conversion rates, kills trades, or delays critical actions.
PCI, HIPAA, GDPR, or the AI Act may not explicitly mandate AI protection, but if an unblocked injection or exfiltration incident occurs, enterprises face fines, failed audits, and regulatory scrutiny. Over-blocking creates its own risks too, many teams disable blocking mode entirely, leaving environments non-compliant.
Because legacy WAFs weren’t designed for AI’s dynamic, non-deterministic flows, enterprises burn resources trying to compensate. Full-time engineers babysit brittle rule sets. SOC teams chase noisy false positives. Extra servers or higher cloud tiers are purchased just to offset latency.
AI systems often chain multiple calls : an agent triggers an MCP, which queries a vector DB, which calls APIs. Add 20–50ms of WAF latency to each hop and the whole experience drags.
AI attack protection that safeguards
experience
Transparent rules that teams can trust
Levo exposes every blocking rule in plain sight, no black-box logic. Security, compliance, and engineering teams can see, edit, and reason about protections against prompt injections, agent misuse, or MCP abuse.
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Out of the box protections, instantly extendable
AI-specific protections ship pre-configured: from model extraction attempts to excessive vector queries. Enterprises can extend coverage on demand either by requesting new rules from Levo or customizing their own.
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API driven control of AI protections
Blocking isn’t static, AI traffic shifts fast. Levo’s control-plane APIs let enterprises enable, disable, or refine rules programmatically as agents evolve. From toggling protections mid-experiment to automating enforcement in CI/CD, security stays aligned with velocity.
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Levo unites every team to defend against the biggest risk: obsolescence from slow AI adoption.
With accurate, low latency protection, engineering teams deploy AI features confidently. Runtime safeguards catch malicious activity without breaking user flows, freeing engineers from late night fire drills and letting them focus on innovation that drives revenue.
High fidelity blocking rules stop real attacks while filtering out false positives. Security leaders move from constant triage to proactive threat management, reporting fewer breaches, cleaner metrics, and stronger protection without burning out their teams.
Immutable, transparent rules and audit grade evidence show regulators that every AI transaction is governed. Compliance leaders no longer chase exceptions or remediate audit findings, they demonstrate continuous control and reduced liability with every report.
Block attacks without blocking business momentum
Frequently Asked Questions
Got questions? Go through the FAQs or get in touch with our team!
What are Levo Inline Guardrails?
Inline Guardrails are real-time controls that enforce allow-deny-redact decisions on AI traffic. They use Levo’s runtime context to stop risky actions, redact sensitive data, and keep AI apps safe without breaking delivery.
Why do we need inline protection if we already have detection?
Detection tells you what is happening. Inline protection changes what happens next. It blocks or redacts in the same flow, cutting dwell time and preventing incidents from turning into breaches.
What can Inline Guardrails enforce?
Allow, deny, and redact policies across prompts, outputs, embeddings, vector queries, tool calls, MCP functions, API requests, and model routing. Policies can target identities, data classes, vendors, and regions.
How does redaction work in practice?
Guardrails scrub PHI, PII, secrets, and regulated markers from inputs and outputs, including prompt bodies, retrieved context, and API payloads. Redaction happens locally so payloads do not leave your environment.
Can Guardrails stop prompt injection and jailbreaks?
Yes. Inputs are normalized and scored for injection patterns. Outputs are checked for policy violations. Suspicious chains can be sandboxed, responses can be replaced with safe fallbacks, and sessions can be killed.
How are agent tools and MCP calls controlled?
Guardrails enforce tool and function allowlists, validate schemas, constrain parameters, and require least-privilege scopes. Unsafe tool invocations are denied or rewritten per policy.
Do Guardrails help with insider misuse?
Yes. Policies tie actions to identities and token scopes. Over permissive requests, risky destinations, or bulk extraction attempts are stopped or throttled in real time.
Can we enforce vendor and region rules?
Yes. Policies like “No PHI to non US models” or “Block uploads to unapproved vector stores” are applied inline. Non compliant routes are denied or auto-rerouted to approved vendors.
What about loops, runaway tasks, or cost spikes?
Guardrails detect excessive token use, recursive chains, and long running sessions. They can throttle, cap, or end the session before cost and risk accumulate.
How are false blocks avoided?
Decisions are context aware and explainable. You can run policies in monitor or shadow block first, then promote to enforce when signal is proven.
What is the latency impact?
Minimal. Enforcement is lightweight and designed for production paths. You choose where to place controls so critical flows are protected without added friction.
Where do Guardrails run?
At logical choke points like gateways and proxies, LLM and MCP boundaries, and API ingress paths. Policies use runtime context from Levo visibility so actions match real conditions.
How are policies defined?
As Policy as Code. Teams write readable rules to deny redact, destinations, data classes, identities, and vendors. Policies are versioned, testable, and CI friendly.
Can we stage rollouts safely?
Yes. Use monitor only, shadow block, and canary modes. Promote to enforce after you verify impact on real traffic.
What evidence is recorded for audits?
Immutable trails show who acted, what was blocked or redacted, why the rule triggered, and where the data was headed. Evidence is exportable for regulators and customers.
How do Guardrails work with Detection and Red Teaming?
Detection finds real risks, Guardrails stop them, Red Teaming turns them into repeatable tests. The loop hardens posture continuously using runtime truth.
How does this help Engineering?
Fewer noisy tickets, fewer rollbacks, safer defaults. Teams ship faster because risky behavior is contained automatically.
How does this help Security?
Real time control at the points that matter, clear reasons for each action, and fewer pages spent triaging noise.
How does this help Compliance?
Continuous enforcement of data handling and residency rules with audit grade evidence on demand.
What outcomes should we expect?
Lower incident rates, reduced analyst load, controlled spend, and faster, safer AI deployments that meet regulatory expectations.
What is the UVP in one line?
Inline guardrails that are non-invasive, high signal, context aware, runtime aware, and fully configurable to protect the entire AI control plane.
How do we get started?
Cut through noise, eliminate threats, eradicate budget wastes. Turn on Inline Guardrails and enforce allow-deny-redact where it matters.
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