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We are excited to announce the launch of Levo.ai’s AI Firewall, designed to protect custom, in-house AI applications end-to-end, enabling enterprises to move from pilots to production rapidly, securely, and with confidence.
Enterprises are increasingly building and operating their own AI applications because the upside is strategic.
In one enterprise survey, 78% of companies said they are using or plan to use in-house generative AI solutions, and 52% reported a hybrid approach in which internal teams use third-party components to build an in-house solution.
First-party AI becomes a competitive lever as it is tailored to proprietary workflows, internal data, and differentiated customer experiences.
But scaling AI exposes a hard reality. Security and compliance gaps are the top reasons adoption slows for 37% of enterprises.
AI systems can take actions dynamically, and traditional security controls were not built to govern such runtime decision making.
That is the gap that Levo's AI Firewall fills. It helps protect enterprise AI against novel attacks like jailbreaks, prompt injection, and sensitive data exfiltration.
As a result, leaders can deploy AI-powered features such as copilots, agents, and partner-facing experiences with stronger protection and clearer accountability.
How Levo's AI Firewall Works
Levo’s AI Firewall is deployed as an inline ingress control at the point where AI applications are exposed to users, partners, and external systems.
From this position, it evaluates AI traffic in real time and allows teams to monitor activity, raise alerts, or block requests based on risk.
What differentiates Levo is the scope of what it protects. Many AI security tools focus on a single layer, such as the prompt or the model.
But that’s not how AI operates. A single request often sets off a sequence of automated steps: an agent retrieves internal data, calls APIs, interacts with other services, and triggers actions across the environment.
These steps deliver speed and automation, but they are also where sensitive data is accessed and business decisions are carried out.
This is why securing one component in isolation is no longer sufficient. In AI-native systems, risk accumulates across the execution path. What begins as a simple interaction can escalate into widespread data exposure or unauthorized actions because the system is designed to operate quickly and autonomously.
Effective protection must therefore extend beyond the entry point and account for the full chain of actions initiated by an AI request.
Levo’s AI Firewall is built for this reality. It protects the entire AI transaction, from the initial entry point to every downstream tool, API, MCP Server, LLM Application, RAG or service call that an AI agent triggers.
By safeguarding not just what enters an AI application but also the actions that follow, the firewall reduces uncertainty for leadership.
AI initiatives can move forward faster, with fewer last-minute approvals, fewer stalled rollouts, and far less risk of high-impact incidents that damage trust or brand reputation.
AI Firewall Coverage: 12 Protection Layers
Levo’s AI Firewall provides comprehensive protection against the most common and damaging attack patterns in today's AI applications. Coverage is applied inline, with controls that can detect, alert on, or block unsafe behavior as it occurs.
1. Ingress AI Protection for Exposed Endpoints
Levo protects AI applications at the point where they are exposed to users, partners, or external systems. Every inbound request is inspected inline so unsafe behavior can be detected, alerted on, or blocked before it reaches agents, models, or internal services.
2. Prompt injection defense
The firewall inspects user prompts for attempts to override instructions or manipulate system behavior. Requests can be blocked, rewritten, or wrapped with hardened system prompts to prevent unintended downstream actions.
3. System Prompt Leakage Protection
Levo prevents disclosure of system prompts, internal instructions, and hidden control logic. Responses are filtered to redact or block content that could reveal how the AI system is configured or governed.
4. Sensitive data protection for AI outputs
Before responses are returned to users, the firewall scans for sensitive data such as personal, financial, or proprietary information. When detected, data can be masked, redacted, blocked, or routed into incident workflows.
5. AI Firewall Audit Trail and Compliance
Every decision made by the firewall is recorded with clear correlation IDs. This creates an auditable trail of AI activity and enforcement actions, while allowing payload logging to be tuned to balance cost and compliance needs.
6. Complete Runtime AI visibility
Levo provides continuous visibility into how AI applications behave in production. Teams can see how requests flow through agents and systems, enabling faster investigations and stronger governance without disrupting workloads.
7. Model Extraction And Scraping Protection
The firewall detects abnormal usage patterns that indicate attempts to scrape or reverse engineer AI services. These behaviors can be throttled or blocked, with alerts raised before large scale extraction occurs.
8. Denial-Of-Service and Abuse controls
Levo protects against prompt flooding and resource exhaustion attacks by enforcing rate limits, request size caps, concurrency limits, and temporary bans. This prevents runaway costs and service degradation.
9. Agent Tool Use Policy Enforcement
When agents invoke internal tools or services, Levo enforces allowlists and parameter-level controls. This prevents agents from accessing or modifying systems beyond what is explicitly permitted.
10. Toxic and Off-Brand Content Moderation
Both prompts and responses are evaluated for harmful, inappropriate, or off-brand content. Unsafe content can be blocked or transformed to ensure AI interactions align with company values and policies.
11. Indirect Prompt Injection (Shadow Prompting)
Levo scans retrieved documents, tool outputs, and external content that may be injected into prompts. Malicious instructions hidden in this context can be stripped, quarantined, or blocked before influencing the model.
12. Jailbreaks and Multi-Turn Manipulation
The firewall detects gradual attempts to bypass safeguards over multiple interactions. When identified, sessions can be terminated, restrictions tightened, or responses forced into safe mode.
Custom Guardrails and Policy Enforcement
Levo’s AI Firewall is configurable, so enterprises can set guardrails that align with their data, workflows, and compliance requirements.
Example: HIPAA guardrails using custom policies
A healthcare enterprise can use YAML or WASM to define the auditable boundaries of what is permitted around PHI (approved systems, allowed destinations, and default handling such as redaction or blocking). Custom code adds the situational logic needed for HIPAA workflows, such as rules that depend on role, context, and intent, without forcing teams to change application code each time policies evolve.
What Makes Levo's AI Firewall Enterprise Grade
Levo’s AI Firewall is built around what enterprises care about most: production safety, fast rollout, and complete coverage of the AI app they are putting in front of users.
- Production-grade performance on the critical path: Inline enforcement stays fast and stable, without per-request callouts that introduce unpredictable latency or failures. Teams can enable real blocking in production without degrading user experience or triggering avoidable outages.
- Faster deployment with built-in AI and API context: Levo does not require months of manual mapping before you get value, because it already discovers AI entry points and the APIs those experiences rely on. Deployment turns from a multi-month integration into a straightforward week long exercise.
- Full boundary coverage, including third-party inside the AI app: Levo secures first-party agents and MCP servers as well as the partner tools, plugins, and integrations that expand the surface area. Integrations are therefore rolled out faster, with fewer blind spots and fewer exceptions that stall rollout.
Deploying AI Firewall in Enterprise Environments
Levo’s AI Firewall is designed to be deployed by security teams without asking engineering to rewrite applications or embed new SDKs. It installs with a single command and is managed centrally, so policy updates and tuning do not require per-service changes.
Rollout is safe by design: teams can move from monitor to alert to block as confidence builds.
Operational risk stays low because enforcement is built to avoid “security tooling broke production” scenarios.
Levo can be deployed on-prem or delivered through a SaaS operating model, depending on how an enterprise wants to run security controls.
In either model, inspection and enforcement happen inside the customer environment, so sensitive prompts and responses never leave enterprise boundaries.
That same design avoids costly traffic mirroring and phone home models, which is why enterprises typically save $500,000 to $1,000,000 annually in cloud and total cost at scale.
The result is a faster path from pilot to production, with fewer bottlenecks, fewer compliance escalations, and stronger confidence in what AI can safely power.
Speak to en engineer today to ship AI to production with confidence, not compromise
