Smartflow Reviews & Product Details

APERION SmartFlow is an on-premises runtime governance control plane for enterprise AI. It sits inline on the call path between AI agents and any model and inspects every prompt, response, and Model Context Protocol (MCP) tool call before it completes. Security and risk teams use SmartFlow as an AI gateway, AI firewall, and policy engine in one runtime layer, so that AI agents and large language model applications can run inside regulated environments under continuous, enforceable control. SmartFlow is Kubernetes-native, runs inside the customer environment including air-gapped and sovereign deployments, and exposes no model traffic to a third party. SmartFlow is the runtime governance layer of the APERION Enterprise AI Trust Fabric. It is built for the chief information security officer and chief risk officer in financial services, banking, insurance, healthcare, life sciences, pharmaceuticals, defense, and the public sector. The problem SmartFlow solves Enterprise AI is moving from chat to agents. Agents act on behalf of people, at scale, with delegated authority. They send prompts to models, receive responses, and call tools that read and write to real systems. The controls built for humans using applications do not govern agents acting on behalf of humans. The result is a runtime gap: sensitive data reaching public models, prompt injection steering an agent off task, shadow AI outside any policy, and destructive tool calls that existing API gateways and data loss prevention tools never inspect. Detection and response tools report these failures after they happen. By then a regulated record has already left the environment. SmartFlow closes the gap with prevention on the call path. It reads the prompt before it leaves, evaluates the response before it returns, and inspects the tool call before it runs. What SmartFlow does AI gateway: routes across model providers so teams keep model optionality without rewriting applications. Supports commercial and self-hosted models behind one governed endpoint. AI firewall and policy enforcement: inline inspection of every prompt and response, with detection and redaction of personally identifiable information (PII), regulated payloads, secrets, and proprietary content before anything leaves the perimeter. MCP tool-call governance: inspects each Model Context Protocol tool call, scores intent and parameters, and stops destructive or unauthorized actions before execution. Pairs with APERION Shield, the open-source MCP middleware. AI agent identity: cryptographic identity for agents, so every action is attributable and every agent operates under a known principal rather than an anonymous service account. Verified-human step-up: for high-consequence actions, SmartFlow can hold the call and require a verified human before it proceeds. Semantic caching: reduces token cost and latency by serving semantically equivalent responses from cache. Identity-bound audit and evidence: every prompt, response, and tool call lands in a tamper-evident record bound to a verified human, built for regulatory examination rather than reconstructed after an incident. How it works SmartFlow terminates the connection between an AI agent and the model or tool it is trying to reach. On every call it runs a multi-pass policy evaluation: classification, sensitive-data detection, prompt-injection checks, and intent scoring on tool calls. Based on policy it can allow, block, redact, route to a different model, or require a verified-human step-up. Decisions are enforced inline, at low added latency and recorded in full. The same rules apply whether the traffic comes from an API-connected agent, an internal application, or an MCP tool call. Runtime governance versus detect-and-respond SmartFlow is inline runtime governance, not AI detection and response (AIDR). Detection observes activity and alerts after the fact. Runtime governance enforces policy before the action leaves the environment. For regulated workloads where a single prompt can move a customer record to a public model, prevention on the call path is the control a regulator expects. Mature programs run both: SmartFlow for the actions that cannot be allowed to happen, and detection for broad visibility. Deployment On-premises, private cloud, and air-gapped. SmartFlow runs inside the customer environment with no cloud dependency and no third-party exposure of model traffic. Kubernetes-native, containerized, and built to operate inside the network perimeter of a bank, a hospital system, or a defense environment. Sovereign deployment for data-residency and national-security requirements. Compliance and regulatory evidence SmartFlow produces identity-bound, tamper-evident audit evidence mapped to the frameworks regulated enterprises answer to, including the EU AI Act, the NIST AI Risk Management Framework, ISO 42001, FINRA, FFIEC, SR 11-7 model risk guidance, NYDFS Part 500, DORA, HIPAA, and 21 CFR Part 11. Evidence is queryable, so examination questions are answered from a record rather than a forensic reconstruction. The record names the verified human accountable for each AI action, which is what an examination asks for and what a log alone cannot provide. Integrations Identity and access: composes with Okta, Microsoft Entra, Active Directory, Veza, and SGNL rather than replacing them. SmartFlow adds the runtime and evidence layers above existing access governance. Workflow agents: composes with workflow-plane platforms such as ServiceNow and Microsoft agent orchestration. SmartFlow governs what those agents send to the model. Open source: APERION Shield, Apache 2.0 licensed, is the open-source MCP middleware front door to the platform and integrates directly with SmartFlow. Who SmartFlow is for SmartFlow is built for security and risk leaders deploying agentic AI in regulated industries: financial services, banking, insurance, capital markets, healthcare, life sciences, pharmaceuticals, defense, and the public sector. Typical buyers are the CISO, the CRO, heads of AI governance, and model-risk and compliance teams who need to deploy AI agents and prove to a regulator what every agent did, on whose behalf, and under which policy. The APERION Trust Fabric SmartFlow is one layer of the APERION Enterprise AI Trust Fabric, a four-layer architecture for governing enterprise AI agents: verified identity (NIST IAL2/AAL2 identity proofing), access governance (integrating existing identity platforms), runtime governance (SmartFlow), and audit and evidence (the APERION Regulatory Examination Suite). SmartFlow owns the runtime and evidence layers and binds identity through every action. Frequently asked questions Is SmartFlow an AI gateway or an AI firewall? Both, in one runtime layer. It routes across model providers like a gateway and inspects and enforces policy on prompts, responses, and tool calls like a firewall, with identity-bound audit underneath. Does SmartFlow run on-premises? Yes. SmartFlow is Kubernetes-native and runs inside the customer environment, including air-gapped and sovereign deployments, with no third-party exposure of model traffic. How is SmartFlow different from AI detection and response? Detection alerts after an action occurs. SmartFlow enforces policy inline before the action leaves the environment, and produces identity-bound evidence for examination. Does SmartFlow replace our identity platform? No. It composes with Okta, Entra, Active Directory, Veza, and SGNL, and adds runtime governance and evidence above them. What does SmartFlow do for regulatory examinations? It produces tamper-evident, identity-bound records mapped to frameworks including the EU AI Act, NIST AI RMF, FINRA, and HIPAA, answerable from a query.