Levo’s AI Gateway to Govern Third-Party AI

Enforcement requires a centralized layer that can allow approved destinations and block unapproved tools across AI chat and AI enabled SaaS. Without that layer, “approved AI” remains a guideline rather than a control, and adoption naturally fragments. Strong enforcement also needs identity and context so policies reflect real business risk and do not become overly restrictive. Many organizations also need credential governance so usage does not depend on unmanaged accounts and scattered tokens. When provider governance is enforceable, security and procurement stop being the bottleneck for productivity. Levo’s AI Gateway enforces approved providers and destinations while supporting segmented policies that reflect enterprise risk realities.

Key risks include uncontrolled sharing of regulated data, inability to prove controls, and inconsistent enforcement across teams and regions. Auditors typically ask for evidence that policies exist and that they are enforced consistently, not just written down. Without auditability, approvals stall, and the organization either slows adoption or accepts silent exposure. Compliance challenges also grow when AI is used across multiple tools, accounts, and SaaS workflows because evidence becomes scattered and incomplete. The safest programs make governance measurable, repeatable, and demonstrable across the organization. Levo’s AI Gateway produces audit-ready evidence and enforces policy so compliance teams can approve adoption with confidence.

Costs rise quickly when usage expands across teams and tools without guardrails. Strong cost governance includes usage controls, quotas, and attribution by team or function so spend maps to business value. Without this, AI budgets expand faster than measurable outcomes, which triggers leadership caution and slowdowns. Cost also becomes harder to manage when teams subscribe independently or use unmanaged accounts that bypass procurement visibility. The goal is to keep adoption broad while making spend predictable and defensible. Levo’s AI Gateway adds quotas, usage controls, and attribution so third-party AI spend stays measurable and controlled as adoption grows.

Many AI gateways introduce workflow latency or vendor security concerns. If enforcement relies on remote callouts, or if prompts and responses are exported for analysis, trust drops and bypass grows. Another risk is operational friction, where deployment requires slow endpoint rollouts or heavy application changes, which delays adoption and weakens coverage. A successful gateway needs predictable performance, centralized control, and a rollout model that builds confidence before enforcement becomes strict. The right design keeps governance inside the enterprise boundary, so security posture improves without introducing new vendor risk. Levo’s AI Gateway is designed for predictable performance and keeps sensitive AI context within the customer environment, reducing both latency concerns and third-party exposure.

Governance is incomplete if it only controls what goes out. Enterprises also need safeguards for what comes back because outputs can quickly enter documents, tickets, customer communications, and operational workflows. Without controls, errors can be propagated at scale, creating reputational and operational risk. Strong governance inspects responses, applies policy actions, and creates traceability so organizations can understand what was generated and why. This reduces the risk of “silent contamination” of business workflows with untrusted output. Levo’s AI Gateway supports response controls and traceability so AI outputs remain safe to use across enterprise workflows.

Enterprise risk is not uniform, and policy must reflect business context. Regulated functions often require tighter controls than low-risk use cases, and jurisdictions frequently impose different privacy and data handling requirements. If governance is one size fits all, it either blocks too much and slows adoption or allows too much and increases exposure. Segmented enforcement is what makes adoption scalable because it enables AI where it is appropriate and constrains it where it must be constrained. This is also how governance remains defensible in audits across regions and business units. Levo’s AI Gateway enables segmented policies by team, context, and region so governance aligns to real enterprise operating requirements.

An AI gateway governs third-party AI usage when employees or systems call external tools and models. An AI Gateway protects in-house AI applications end to end when an enterprise exposes AI features to users and customers. As adoption expands, both boundaries matter because third-party usage creates data egress and policy risk, while in-house AI creates runtime workflow and action risk. Many organizations start with a gateway to govern employee usage and then extend to firewall protections as they build customer facing AI. When both are covered, governance becomes end to end rather than fragmented. Levo supports both boundaries as part of a unified AI security platform, enabling a consistent governance story as AI expands.

The most reliable path starts with visibility and baseline usage without disrupting workflows, then moves into phased enforcement. This reduces false positives, builds internal trust, and aligns stakeholders early across IT, security, and compliance. Standardizing approved tools and audit evidence from day one prevents governance from becoming a retroactive cleanup project. Enterprises should also define what “safe usage” means by team, data type, and region so governance reflects real operating needs. A structured rollout is what prevents bypass and keeps adoption growing responsibly. Levo’s AI Gateway supports a discover, baseline, enforce rollout model and provides the controls and evidence needed to scale quickly and safely.

Some approaches depend on endpoint agents, which slow rollouts and add operational burden across fleets. That friction often results in uneven coverage, delayed enforcement, and inconsistent governance across business units. Central governance is often preferred because it delivers immediate consistency without waiting for device deployment cycles. A gateway should scale with the organization, not with the number of endpoints managed. The best approach reduces dependency on user behavior and local installs. Levo’s AI Gateway is designed for centralized governance without endpoint agents, enabling faster coverage and faster time-to-control.

If a gateway becomes a bottleneck, employees bypass it and governance fails. Latency often appears when enforcement requires external round trips, heavy processing, or unreliable routing that interrupts normal workflows. Strong gateways keep enforcement predictable and efficient at high scale so user experience remains intact. The goal is to preserve the very productivity gains that justify AI adoption. When experience stays smooth, compliance increases naturally because bypass pressure drops. Levo’s AI Gateway is engineered for predictable performance so governance does not become a productivity tax.

When evaluating AI Gateway vendors, the key question is whether governance reduces enterprise risk or simply relocates it to another vendor. Many solutions require routing prompts, responses, or usage telemetry through external systems, which can introduce new data handling concerns, approval delays, and regulatory complications. A strong AI Gateway should preserve enterprise boundaries, support clear data residency expectations, and make it easy to prove what data is processed where. It should also provide controls that work at scale without creating new blind spots or new trust dependencies. Levo’s AI Gateway keeps governance within the customer environment and avoids exporting sensitive AI context, reducing third-party exposure while still enforcing enterprise policy.

Enterprises should prioritize capabilities that make governance scalable, not manual. The best AI Gateways support segmented policies by team, region, and use case, while keeping those policies consistent and auditable across the organization. They should make enforcement repeatable and adaptable as business needs change, without requiring constant human review for every exception. Evaluation should also consider how quickly controls can be rolled out, how easily they can be tuned, and whether the system produces audit-ready evidence by default. Levo’s AI Gateway supports customizable guardrails with centralized control so policies can evolve quickly without turning governance into operational overhead.