A security deployment method, device and medium for enterprise-level applications

Abstract

The embodiment of the specification discloses a security deployment method, equipment and medium for enterprise-level application, relates to the technical field of security deployment, and the method comprises the following steps: acquiring application topology graph data generated by a preset visual development platform, splitting the application topology graph data into a set of atomized micro-service units by a quantumization deconstruction engine, and generating a micro-service dependency tree; scanning a target deployment environment, extracting environment characteristic fingerprints of the target deployment environment, generating environment adaptation layer code according to the environment characteristic fingerprints; injecting the environment adaptation layer code into each atomized micro-service unit, generating an environment-aware micro-service package, and constructing a cross-cloud communication link matrix based on the micro-service dependency tree; based on the environment-aware micro-service package and the cross-cloud communication link matrix, performing a deployment verification test in a sandbox environment, generating a deployment verification report, and distributing the environment-aware micro-service package to target deployment environment nodes according to the deployment verification report.

Description

Technical Field

[0001] This specification relates to the field of application deployment technology, and in particular to a secure deployment method, device and medium for enterprise-level applications. Background Technology

[0002] As enterprises accelerate their digital transformation, low-code development platforms, with their visual modeling and modular component capabilities, have significantly improved the efficiency of building business applications.

[0003] However, in the enterprise application deployment phase, the existing platform deployment process is disconnected from the development phase, requiring manual export of application packages and manual configuration of environment dependencies, resulting in a high deployment error rate. Furthermore, its adaptability to multi-cloud / hybrid cloud environments is weak; API differences between different cloud service providers, container orchestration framework version conflicts, and network policy changes necessitate rewriting adaptation code, extending the deployment cycle. Additionally, the traditional RBAC permission model struggles to handle dynamic communication scenarios in microservices, and the lack of a unified encryption policy for cross-cloud data transmission poses risks of unauthorized access and data leakage. Although some solutions attempt to introduce Infrastructure as Code (IaC) technology, its environment description files still require manual maintenance, hindering the efficient and secure delivery of enterprise applications.

[0004] Therefore, traditional enterprise application deployment requires manual configuration of different environments and involves complex deployment processes, which cannot meet the dual requirements of enterprise applications for development efficiency and delivery quality. Summary of the Invention

[0005] This specification provides one or more embodiments of a secure deployment method, device, and medium for enterprise applications, which addresses the following technical problem: traditional enterprise application deployment requires manual configuration of environmental differences and involves complex deployment processes, failing to meet the dual requirements of enterprise applications for development efficiency and delivery quality.

[0006] One or more embodiments of this specification employ the following technical solutions:

[0007] This specification provides one or more embodiments of a secure deployment method for enterprise-level applications. The method includes: acquiring application topology data generated by a preset visualization development platform, and using a quantum deconstruction engine to split the application topology data into a set of atomic microservice units to generate a microservice dependency tree; scanning a target deployment environment and extracting the environment feature fingerprint of the target deployment environment, and generating environment adaptation layer code based on the environment feature fingerprint, wherein the environment feature fingerprint includes cloud service provider API type, container orchestration framework version, and network configuration policy; injecting the environment adaptation layer code into each of the atomic microservice units to generate an environment-aware microservice package, and constructing a cross-cloud communication link matrix based on the microservice dependency tree; performing deployment verification tests in a sandbox environment based on the environment-aware microservice package and the cross-cloud communication link matrix, generating a deployment verification report, and distributing the environment-aware microservice package to the target deployment environment nodes according to the deployment verification report.

[0008] This specification provides one or more embodiments of a security deployment device for enterprise applications, including:

[0009] At least one processor; and,

[0010] A memory communicatively connected to the at least one processor; wherein,

[0011] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the above-described method.

[0012] This specification provides one or more embodiments of a non-volatile computer storage medium storing computer-executable instructions configured to perform the above-described method.

[0013] The at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: Through the technical solutions of the embodiments of this specification, the problems of deployment fragmentation, weak environment adaptation, and lack of security verification are fundamentally solved by atomic decoupling of environment topology, cryptographic abstraction of runtime features, and verification-driven security closed loop. At the environment adaptation level, based on the weight analysis of application topology by the quantum deconstruction engine, precise decoupling of business logic and infrastructure is achieved, and the traditional manually written environment adaptation scripts are transformed into atomic encapsulations of highly cohesive business modules, enabling microservice units to have environment-aware independence; combined with the encrypted extraction mechanism of environment feature fingerprints, the adaptation layer code is dynamically compiled and generated through the rule base, eliminating the hard-coded dependency of multi-cloud differences in principle, and realizing the machine-executable environment configuration. At the security verification level, the sandbox environment mirrors the production topology based on environmental feature fingerprints, enabling the verification of cross-cloud communication link matrices to move from theoretical strategies to physical simulations. By traversing all communication paths using a simulated dataset reconstructed from historical traffic, it exposes dynamic policy gaps that traditional RBAC models cannot cover. The proactive injection test of illegitimate tokens provides runtime stress verification of the zero-trust policy chain, elevating static permission rules to behavioral-level interception capabilities. The resulting deployment verification report becomes a secure and trusted middleware connecting testing and production. In the distribution and deployment phase, the strong binding of digital signatures and verification reports establishes a secure traceability chain for microservice packages at the cryptographic level. The distribution routing table generated based on environmental feature fingerprints essentially compiles network topology policies into a transport control plane, achieving dual trust transfer between the transport and application layers through nested encryption of bidirectional TLS and AES-GCM. Ultimately, when the container orchestration framework is triggered, the environment adaptation layer code has pre-configured multi-cloud compatibility, transforming deployment behavior from manual configuration to declarative scheduling. Atomic decoupling addresses environmental heterogeneity, cryptographic abstraction ensures adaptability determinism, and secure closed-loop verification controls risks before deployment through simulation, interception, and tracing mechanisms, achieving secure deployment throughout the entire process. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0015] Figure 1 A flowchart illustrating a security deployment method for enterprise applications provided in the embodiments of this specification;

[0016] Figure 2This is a schematic diagram of a security deployment device for enterprise applications provided as an embodiment of this specification. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0018] This specification provides a security deployment method for enterprise-level applications. It should be noted that the execution entity in this specification can be a server or any device with data processing capabilities. Figure 1 This specification provides a flowchart illustrating a security deployment method for enterprise applications, as illustrated in the embodiments below. Figure 1 As shown, the main steps include the following:

[0019] Step S101: Obtain the application topology graph data generated by the preset visualization development platform, and use the quantum deconstruction engine to split the application topology graph data into a set of atomic microservice units to generate a microservice dependency tree.

[0020] In low-code development scenarios for enterprise applications, the business process configuration data generated by the visual development platform, including component layout, event binding, and data flow pointers, is essentially an intuitive mapping of business logic. However, it lacks a structured description of the underlying service architecture. Directly deploying such raw data will lead to implicit dependencies between business components, such as shared database connection pools and transaction consistency requirements, which cannot be explicitly identified, causing service call chain breaks after deployment. In addition, the lack of quantification of computing resource requirements, such as the difference between the high computing power consumption of AI components and the low load of form components, leads to an imbalance in resource allocation.

[0021] The process of acquiring application topology data generated by a pre-defined visualization development platform includes: receiving business process configuration data sent by the visualization development platform, wherein the business process configuration data includes component layouts, event binding relationships, and data flow directions generated by user drag-and-drop; calling the business logic engine to parse the business process configuration data and generate a service call chain diagram; merging the service call chain diagram with the pre-acquired component dependencies to construct weighted application topology data, wherein the node weights in the application topology data are related to the computational complexity of the components, and the edge weights are related to the data traffic frequency.

[0022] In one embodiment of this specification, in a visual development platform, when a user drags and drops pre-defined business components (such as form designers, process nodes, and data connectors) onto the canvas area via a graphical interface, the platform captures the component's position coordinates, attribute configuration parameters (such as database field mapping rules and API endpoint addresses), and logical relationships between components in real time. When the user uses the connector tool to establish an event binding relationship between the "Submit Button" component and the "Database Write" component, the platform automatically generates an event trigger descriptor, recording the event type (such as onClick), source component ID, target component ID, and a list of passed parameters. Simultaneously, the user defines field-level mapping rules through the data flow configuration panel, such as mapping the "user_name" field of a form to the "name" column of a database table; the platform serializes this into a JSON-formatted data flow pointing relationship. These raw interaction data are aggregated into a business process configuration data package by the platform's state management module. This data package contains three core metadata parts: a component layout matrix, an event binding relationship dictionary, and a data flow pointer graph. The component layout matrix is ​​used to store component types, screen coordinates, and size attributes. The event binding relationship dictionary stores the event source component, event type, processing target component, and parameter list in a hash table structure. The data flow pointer graph is a directed graph structure that records the data source component, target component, and transformation rules at the field level.

[0023] After the deployment server receives the data packet via HTTPS, it calls the business logic engine module for deep parsing. The engine first loads a predefined business semantic rule library, such as automatically associating approval nodes with the workflow engine and binding payment buttons to the payment gateway interface, converting the event binding relationship dictionary into a state transition graph. For example, when the event chain of the submit button or approval node is detected, a state verification node is automatically inserted and state transition conditions are generated, such as requiring supervisor approval for amounts > 10000. Subsequently, the data flow graph is parsed, and implicit data dependencies are identified through dependency analysis algorithms. For example, when two components are detected writing to the same database field simultaneously, a transaction lock node is automatically inserted. Finally, a service call chain graph is generated. This graph is stored using an attribute graph structure, with node types divided into UI component nodes (including rendering resource references), business service nodes (including API call specifications), and data storage nodes (including connection string templates). Edge types are divided into control flow edges (annotating execution conditions) and data flow edges (annotating field mapping relationships).

[0024] At this point, the deployment server retrieves the component dependency table from the component registry. This table, pre-configured by the platform administrator, records the technical dependencies of each component. For example, the "Charts component" depends on the ECharts library version, and the "AI prediction component" depends on Python 3.8 runtime. A graph fusion algorithm merges the service call chain graph with the dependency table. Each node in the service call chain graph is traversed. If a component ID associated with a node is identified in the dependency table, the computational complexity rating (divided into light / medium / heavy) of that component is assigned as the node weight. For example, the "video processing component" is marked as heavy load. Simultaneously, the frequency of data flow edges in historical simulations is statistically analyzed and normalized to a value in the range [0,1] as the edge weight. For instance, if an API call edge is triggered 9 times in 10 tests, its weight is 0.9. The final weighted application topology graph data is stored in an adjacency list format. Each entry contains a node ID, node weight value, a list of dependent components, and fields such as adjacent node IDs, edge weights, and transmission protocol type, providing structured input for subsequent quantum deconstruction.

[0025] By converting drag-and-drop operations into an executable service call chain diagram, the call order and protocol constraints between service nodes are clearly defined. Furthermore, predefined component dependencies are merged, and node and edge weights are labeled based on component computational complexity (e.g., GPU acceleration) and data traffic frequency (e.g., transaction count per second threshold). This ultimately constructs a machine-understandable weighted application topology diagram, providing an atomic basis for subsequent quantum deconstruction and mitigating deployment architecture defects caused by ambiguous business logic from the source, ensuring the reliability of enterprise applications in complex heterogeneous environments. Compared to conventional low-code platforms that directly export JSON configurations or static images, the technical solution in this specification achieves a fundamental breakthrough by dynamically generating weighted application topology diagrams. Traditional methods rely on developers manually writing deployment description files, requiring repeated debugging of environment parameters and frequently causing overload in production environments due to neglecting differences in component resource requirements. In contrast, the automated topology construction based on the business logic engine in this specification's embodiment transforms business semantics (e.g., event binding relationships) into a quantifiable service architecture model for the first time, allowing the system to predict resource bottlenecks before deployment, such as identifying high-weight nodes that require exclusive CPU cores.

[0026] Furthermore, the weighting mechanism addresses the long-standing "deployment black box" problem in enterprise applications. Conventional solutions, which allocate resources equally, often result in both idle resources and congestion. The technical solutions described in this specification provide a scientific basis for subsequent atomic microservice decomposition. For example, high-weight nodes can be isolated as microservice units and allocated redundant resources, while low-weight nodes can be merged for deployment to save costs. Simultaneously, explicit dependencies completely avoid unknown failures caused by the lack of implicit call chains in traditional deployments, such as service avalanches caused by undeclared database connections. This is particularly beneficial for the zero-tolerance requirements of high-reliability scenarios such as finance and government.

[0027] In enterprise-level low-code application deployment scenarios, traditional microservice decomposition methods rely on manual experience to define module boundaries. This coarse-grained approach to business modules leads to low resource utilization; for example, forcibly bundling computationally intensive AI components with lightweight form components results in resource waste. Furthermore, cross-cloud communication links lack precise control. When the application topology includes public cloud data analytics modules and private cloud database modules, unoptimized communication paths can cause a surge in cross-cloud latency. There is also the issue of inaccurate security policy binding; static microservice units struggle to adapt to dynamic business changes, leading to lagging access control.

[0028] The application topology graph data is split into a set of atomic microservice units using a quantum deconstruction engine, generating a microservice dependency tree. Specifically, this involves: traversing the node weights of the application topology graph data, identifying computationally intensive node sets, and cutting data flow paths based on preset edge weight thresholds to aggregate related nodes into highly cohesive business modules; generating atomic microservice units for each highly cohesive business module, where each atomic service unit includes a unique unit identifier, input / output interface specification data, and resource quota declaration information; and generating a microservice dependency tree based on the cut data flow paths, where the tree nodes are atomic microservice units, and the data flow direction and protocol type are labeled on the tree edges.

[0029] In one embodiment of this specification, after receiving weighted application topology graph data, the quantum deconstruction engine starts a node weight analyzer to traverse all nodes in the graph. Based on a preset computational load grading threshold for node weight values, nodes with weight values ​​greater than or equal to the high load threshold are selected and marked as computationally intensive nodes. Simultaneously, adjacent nodes with direct data flow connections to these nodes are identified. Subsequently, the path cutting module loads a preset edge weight threshold. It should be noted that this edge weight threshold is dynamically adjusted based on historical deployment performance data, typically taking the median of data flow call frequencies. All data flow edges are scanned, and if the weight of an edge is lower than the threshold, a cutting operation is performed at that edge location, disconnecting the direct connection between the source node and the target node, and inserting an asynchronous message broker node, such as a Kafka message queue.

[0030] After the partitioning is complete, the module aggregation algorithm is activated. First, each computationally intensive node and its closely related neighboring nodes (identified through community discovery algorithms, such as Louvain module degree optimization) are divided into initial clusters. Then, clusters sharing more than a preset proportion of connection edges are merged; for example, two clusters with more than 70% bidirectional data flow edges are merged to form highly cohesive business modules, such as the "User Payment Processing Module," which includes payment computation nodes, transaction record nodes, and risk control verification nodes. Each business module is encapsulated as an atomic microservice unit. The deployment controller assigns a globally unique UUID identifier to each unit and generates standardized OpenAPI 3.0 specification files as interface specification data by parsing the input / output interfaces of the components within the module (such as REST API definitions and gRPC service descriptors). Simultaneously, based on the total computational load of the nodes within the module and historical resource monitoring metrics, such as peak CPU utilization and memory usage baseline, resource quota information is declared, for example, reserving 2 CPU cores / 4GB of memory.

[0031] Finally, relying on the tree generator, the data flow paths after the segmentation are reconstructed. Using atomic microservice units as tree nodes, and based on the data flow direction preserved in the original topology graph (e.g., from "Order Service" output to "Inventory Service" input), a directed tree structure is constructed. Tree edge attributes record the data flow direction (source unit UUID → target unit UUID) and protocol type (e.g., HTTP / 2, gRPC, AMQP). Furthermore, the asynchronous communication paths across segmentation points are labeled with the message middleware type (e.g., edge attribute "protocol").

[0032] "Kafka_Topic_OrderEvent") forms a complete microservice dependency tree, which is stored in a JSON tree structure. Each node contains unit metadata, and each edge contains a communication protocol descriptor.

[0033] The quantum deconstruction engine effectively solves the aforementioned technical problems through a weight-driven automated deconstruction mechanism. It identifies high-load component clusters based on node weights (computational load levels) and segments low-frequency communication paths using edge weights (data flow frequency), intelligently reorganizing the topology into highly cohesive business modules with clear physical boundaries. This atomic deconstruction enables each microservice unit to be environmentally insensitive, decoupling from the infrastructure through pre-defined resource quotas and standardized interface specifications. Simultaneously, the generated microservice dependency tree explicitly defines cross-unit call contracts in a tree structure, laying the topological foundation for building a zero-trust communication matrix. This fundamentally solves the problems of resource mismatch, uncontrollable communication, and inaccurate security policies in enterprise hybrid cloud environments, enabling deployment scenarios where development is done once and deployments are made across multiple clouds.

[0034] By automatically segmenting data flow paths using weighted thresholds, atomic units are dynamically reorganized along with the topology graph. When a user drags and drops a new business node, the deconstruction engine automatically identifies its computational weight and assigns it to a matching module, maintaining module cohesion without manual intervention. Utilizing protocol types marked in the microservice dependency tree, differentiated strategies are dynamically injected into communication paths with varying sensitivities, ensuring security controls precisely match business needs. By mandating resource quota declarations for each atomic unit, deployment automatically matches the target environment's resource pool, mitigating deployment failure risks from the outset. More importantly, when a unit fails, the operations and maintenance system can quickly locate related services along the protocol paths marked on the tree edges, significantly shortening fault diagnosis time.

[0035] Step S102: Scan the target deployment environment and extract the environmental feature fingerprint of the target deployment environment in order to generate environment adaptation layer code based on the environmental feature fingerprint.

[0036] The environmental fingerprint includes cloud service provider API type, container orchestration framework version, and network configuration policy.

[0037] In enterprise application deployment, the heterogeneity and dynamism of the target environment are key characteristics of the deployment process. Hybrid cloud / multi-cloud architecture has become the mainstream choice for enterprises. However, there are significant differences in API signature mechanisms, container orchestration frameworks (various versions of Kubernetes), and network policies (security groups, ACL rules) among different cloud service providers (such as AWS, Azure, and private OpenStack). Traditional deployment requires manual writing of environment adaptation code, which is not only inefficient but also prone to runtime failures due to configuration errors. In addition, enterprise security compliance requirements are becoming increasingly stringent. Scenarios such as finance and government need to ensure that deployment packages strictly match the environment security policies. Static deployment templates cannot detect environment changes in real time, such as certificate rotation and firewall rule updates, leading to security vulnerabilities. In addition to the above problems, the strong coupling between environment configuration and business logic makes it difficult to migrate applications across environments, which restricts disaster recovery and elastic scaling capabilities.

[0038] The process involves scanning the target deployment environment and extracting its environmental feature fingerprint. Specifically, this includes: sending a TLS 1.3 authentication request to the target environment via an encryption probe module to obtain the returned certificate chain and infrastructure metadata; extracting the cloud service provider's API signature format, container orchestration framework version number, and network ACL rules from the infrastructure metadata; calculating a digest value using a preset encryption algorithm on the extracted cloud service provider's API signature format, container orchestration framework version number, and network ACL rules to generate an immutable environmental feature fingerprint. Based on this environmental feature fingerprint, environment adaptation layer code is generated. This includes: matching the environmental feature fingerprint with a preset adaptation rule base, which stores cloud service provider SDK mapping relationships; if the identification result is a public cloud environment, generating the corresponding cloud provider's API call encapsulation layer and credential rotation module to determine the environment adaptation layer code; if the identification result is a hybrid cloud environment, generating a cross-cloud service mesh proxy and two-way certificate authentication module to determine the environment adaptation layer code; and compiling the generated environment adaptation layer code into a dynamic link library and attaching an environment configuration manifest.

[0039] In one embodiment of this specification, the deployment server initiates an encryption probe module to send a TLS 1.3 compliant authentication request to the management endpoint of the target deployment environment, such as the Kubernetes API Server or a cloud vendor's metadata service. The authentication request includes a Server Name Indication (SNI) extension and a pre-configured client certificate. The target environment responds by returning its certificate chain and infrastructure metadata. The certificate chain includes PEM format data of leaf certificates, intermediate CA certificates, and the root CA certificate. The infrastructure metadata may be instance type / region information returned by the cloud vendor's metadata service, or kubelet version / node label returned by the Kubernetes node.

[0040] After verifying the target environment's identity via the certificate chain, the probe module calls the metadata parser to extract key features from the returned data. First, it matches the cloud service provider identifier, such as AWS's X-Amz-Credential signature header format and Azure's x-ms-version value, recording its API signature specification version. Second, it parses the container orchestration framework's User-Agent header, such as kube-apiserver / v1.24.3 to extract the major and minor version numbers. Finally, it reads network configurations, such as the security group's JSON description, to extract the source IP range, port rules, and action policies. The extracted raw feature data corresponding to the cloud API signature format, orchestration framework version number, and network ACL rule text is input into the fingerprint generator. The fingerprint generator uses a preset SHA-3-256 hash algorithm to perform a one-way hash calculation on the feature byte stream. It first concatenates each feature field into a byte array in a fixed order, in the format [cloud API signature length][signature content][orchestration version length][version number][ACL rule length][rule text]. Then, it performs two hash operations and adds a salt value to prevent rainbow table attacks. Finally, it outputs a 64-character hexadecimal string as an immutable environment feature fingerprint.

[0041] Subsequently, the adaptation layer generator loads a preset adaptation rule base. It should be noted that the adaptation rule base stores the mapping relationship between cloud provider identifiers and SDKs in key-value pairs, such as "aws":"boto3==1.26.8". The environmental feature fingerprint is matched with the rule base. If the cloud provider identifier in the fingerprint is unique and the network ACL does not detect the local private IP segment, such as the CIDR address not containing 192.168.0.0 / 16, it is determined to be a public cloud environment. The corresponding cloud provider's API call encapsulation layer and credential rotation module are generated. It should be noted that the corresponding cloud provider's API call encapsulation layer includes a request signature function, error retry logic, and a regional endpoint routing table. The credential rotation module is used to implement the automatic refresh of IAM role temporary tokens. If the coexistence of multiple cloud identifiers or the existence of mixed network policies are detected, such as ACLs containing both public and private IP rules, the environment is identified as a hybrid cloud environment. A cross-cloud service mesh proxy and a two-way certificate authentication module are then generated. The cross-cloud service mesh proxy uses an xDS configuration template built on Envoy and injects a service discovery endpoint. The two-way certificate authentication module automatically issues SPIFFE-formatted SVID certificates and configures an mTLS handshake policy. The generated adaptation layer code (Python / Go source files) is compiled into a platform-independent dynamic link library, such as a Linux .so file, along with an environment configuration manifest, YAML declarations of resource limits, environment variable injection rules, and health check endpoints. Finally, it is packaged into the environment adaptation layer deliverable.

[0042] The technical solution in this specification uses an encrypted probe to dynamically extract environmental fingerprints and automatically generate adaptation layer code, thus constructing an environment-aware and adaptive deployment system. The certificate chain and metadata obtained by the TLS 1.3 probe provide a unique identifier for the environment, while the tamper-proof fingerprint generated by the SHA-3 digest algorithm ensures the reliability of the environment description. The adaptation layer code generated based on the fingerprint matching rule base achieves seamless integration between the deployment package and the target environment, fundamentally solving enterprise-level pain points such as redundant manual configuration, security policy mismatch, and high migration costs caused by environmental differences.

[0043] Conventional solutions rely on manually maintained infrastructure, i.e., code templates, requiring pre-written cloud provider-specific configurations, such as Terraform scripts. They cannot dynamically respond to environmental changes, such as cloud service API version upgrades. The technical solution in this specification's embodiments automatically generates adaptation layer code through real-time environment fingerprint extraction and rule base matching. For example, when the probe detects that the target environment is Azure AKS v1.24, the corresponding SDK encapsulation module is injected immediately to avoid API call failures due to version differences. Traditional deployments use unified security policies, such as fixed IP whitelists, which are difficult to adapt to differences in network policies across different environments. For example, public clouds require DDoS protection, and private clouds require adaptation to physical firewall rules. The technical solution in this specification's embodiments incorporates network ACL rules into the environment fingerprint, generating environment-specific security modules. For example, when a hybrid cloud environment is identified, a bidirectional mTLS authentication gateway is automatically built to ensure end-to-end encryption of cross-cloud communication. Simultaneously, environment-matched IP filtering rules are dynamically injected to block unauthorized access at the source. While existing containerized deployments provide environment abstraction, they still require manual adjustment of infrastructure dependencies such as storage volume plugins and load balancers. The environment adaptation layer code of the technical solution in the embodiments of this specification decouples business code from infrastructure by abstracting cloud service interfaces, enabling one-time construction and seamless multi-cloud deployment, and significantly reducing the transformation cost of cross-cloud migration.

[0044] Step S103: Inject environment adaptation layer code into each atomic microservice unit to generate an environment-aware microservice package, and build a cross-cloud communication link matrix based on the microservice dependency tree.

[0045] In enterprise application deployment scenarios with hybrid cloud / multi-cloud architectures, firewall rules and security group policies differ among different cloud service providers, and manual configuration can easily lead to communication blockages or security vulnerabilities. Furthermore, data flow across public and private clouds requires dynamic adaptation of encryption protocols, and traditional static configuration cannot meet compliance audit requirements. In addition, critical paths in the microservice dependency chain (such as payment verification → inventory deduction) need to be differentiated by QoS level, but standard service meshes lack a traffic scheduling mechanism based on business priorities.

[0046] In one embodiment of this specification, the adaptation layer injector of the deployment server loads an atomic microservice unit package, which includes a Docker image file and a unit descriptor, and reads the environment adaptation layer dynamic link library. The microservice unit image is decompressed to a temporary directory using an image unpacking tool (such as umoci), and the environment adaptation layer library file (such as libadapter.so) is copied to the / opt / adapter path of the image. Dynamic linking instructions are injected into the entry script, such as `export LD_PRELOAD= / opt / adapter / libadapter.so`. Subsequently, the container runtime configuration, such as the Kubernetes PodSpec, is modified to declare resource quotas defined in the environment configuration manifest, such as `limits.cpu:2`, and key mount points, such as ` / etc / secrets`. Finally, the image is repackaged to generate an environment-aware microservice package. This image automatically loads the adaptation layer library at runtime, enabling capabilities such as cloud provider API call encapsulation and credential rotation.

[0047] Based on the microservice dependency tree, a cross-cloud communication link matrix is ​​constructed, specifically including: parsing the edge attributes of the microservice dependency tree, extracting all cross-unit communication paths, and creating a communication channel descriptor for each cross-unit communication path, wherein the communication channel descriptor includes source / target unit identifier, transport protocol, and QoS level; injecting IP whitelist rules and TLS encryption suites into the communication channel descriptor of each cross-unit communication path according to the network configuration policy in the environmental feature fingerprint, and determining the current communication channel descriptor; constructing a communication link matrix of a specified dimension according to the total number of atomic microservice units in the atomic microservice unit set and the current communication channel descriptor corresponding to each cross-unit communication path, wherein the matrix elements of the communication link matrix are used to store the current channel descriptor.

[0048] In one embodiment of this specification, the link matrix generator parses the edge attributes of the microservice dependency tree, traverses all edge records of the dependency tree, and filters out cross-unit communication paths where the source and target units are deployed in different physical environments, such as public and private clouds. A communication channel descriptor data structure is created for each path, with fields including source / target unit identifier, transport protocol, and QoS level. The source / target unit identifier is a direct reference to the microservice unit's UUID; the transport protocol is the original protocol type extracted from the dependency tree edge attributes (e.g., gRPC); and the QoS level is a preset policy mapped according to the protocol type, such as gRPC defaulting to QoS level 1 / AMQP asynchronous messages defaulting to QoS level 2.

[0049] Next, the policy injection module loads the network configuration policy extracted from the environment feature fingerprint. If the network ACL rule contains an IP whitelist declaration, it compiles the rule into an iptables-format injection descriptor. Simultaneously, based on TLS cipher suite requirements, such as TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 specified in the environment fingerprint, it declares the mandatory cipher suite list and Certificate Authority (CA) trust chain path in the descriptor. The channel descriptor that completes policy injection is marked as the current communication channel descriptor.

[0050] Finally, the matrix construction engine initializes an N×N sparse matrix based on the total number N of atomic microservice units. It then traverses all cross-unit communication paths, mapping the source unit UUID to a matrix row index (e.g., UUID hash modulo N) and the target unit UUID to a column index, storing the current communication channel descriptor at the corresponding matrix element position. For non-cross-unit communication (e.g., intra-unit calls) or unit pairs without dependencies, matrix elements are set to null. The generated cross-cloud communication link matrix is ​​stored in Compressed Sparse Row (CSR) format, containing three core arrays: a row pointer array, a column index array, and a value array. The row pointer array records the starting position of each row, the column index array stores the column coordinates of non-null elements, and the value array stores the serialized channel descriptor JSON object, forming a communication strategy topology that can be efficiently queried.

[0051] The technical solutions in the embodiments of this specification construct a cross-cloud communication link matrix, dynamically integrate network policies and security requirements in the environmental feature fingerprint with the communication semantics of the microservice dependency tree, automatically inject IP whitelists based on environmental fingerprints to avoid incorrect blocking of cross-cloud access; select the best encryption suite according to the target cloud environment to achieve security self-adaptation; and automatically allocate high-availability link resources according to QoS level to achieve service-aware routing. This fundamentally solves the technical problems of unreliable communication, difficult security management, and lack of service guarantee faced by enterprise applications when deployed in heterogeneous infrastructures.

[0052] Step S104: Based on the environment-aware microservice package and the cross-cloud communication link matrix, perform deployment verification tests in the sandbox environment, generate a deployment verification report, and distribute the environment-aware microservice package to the target deployment environment nodes according to the deployment verification report.

[0053] In a hybrid cloud architecture, there are adaptation risks due to environmental heterogeneity, hidden faults caused by distributed links, and compliance risks arising from blurred security boundaries. If unverified microservice packages are deployed directly to the production environment, environment adaptation issues may lead to service startup failures due to differences in cloud provider API versions or network policy conflicts; cross-cloud communication failures are difficult to locate in complex links, causing business interruptions, and the exposure of security vulnerabilities may result in data leaks or compliance penalties.

[0054] Based on the environment-aware microservice package and the cross-cloud communication link matrix, deployment verification tests are performed in a sandbox environment to generate a deployment verification report. Specifically, this includes: determining a simulated traffic dataset based on historical business traffic log data, loading the environment-aware microservice package and the cross-cloud communication link matrix into an isolated sandbox, injecting the simulated traffic dataset to traverse all communication paths to determine the path connectivity of each communication path; sending test requests carrying illegal tokens to each microservice unit to detect the interception rate and generate policy interception logs; and generating a deployment verification report containing a list of security vulnerabilities based on the path connectivity and the policy interception logs.

[0055] In one embodiment of this specification, the sandbox test controller of the deployment server first extracts raw traffic logs from the enterprise's historical business database. The raw traffic logs include fields such as timestamp, source / target service identifier, request parameters, response status code, and payload size. After filtering invalid records such as health check requests by the traffic cleaning module, the business scenario sequence is reconstructed using a time window aggregation algorithm. For example, consecutive user login → product query → order submission operations are merged into a session stream to generate a simulated traffic dataset with business semantics. Each record in the simulated traffic dataset includes the source microservice UUID, target microservice UUID, protocol type, request payload template, and expected response mode.

[0056] Subsequently, an isolated sandbox environment built on lightweight containers (such as Firecracker microvirtual machines) is launched. This sandbox is pre-configured with a network policy stack consistent with the target environment, replicating the VPC configuration through environment feature fingerprints. All environment-aware microservice packages are loaded in parallel within the sandbox. Each microservice package runs as an independent Pod, mounts an adaptation layer library, and injects a cross-cloud communication link matrix, loaded as a ConfigMap into each Pod for the communication broker to read. The test engine performs a two-phase verification. In the first phase, path connectivity testing, the traffic generator sends requests sequentially according to the simulated dataset. For each communication path (e.g., order service → ...), ...

[0057] (Inventory service), the generator constructs HTTP / gRPC requests conforming to the OpenAPI specification, such as POST.

[0058] ` / inventory / deduct` contains SKU numbers and quantity parameters. It routes to the target unit via a service mesh proxy within the sandbox based on the link matrix. The monitor captures response status, end-to-end latency, and error codes, such as HTTP 200 success or 504 timeout responses, and marks the path connectivity status, including success, failure, and degradation. In the second phase of security policy testing, security probes send test requests carrying illegitimate tokens to the authentication endpoint (e.g., ` / auth / verify`) of each microservice unit. The illegitimate token pool includes expired JWTs, tampered-signature tokens, and RBAC declarations indicating out-of-bounds permissions, triggering the zero-trust policy chain's interception behavior. The policy executor records the interception response, such as 401 Unauthorized or 403 Forbidden, and calculates the interception rate, which is calculated as the number of successful interceptions divided by the total number of tests. Simultaneously, it generates a policy interception log containing the attack vector type, interception timestamp, and policy hit point.

[0059] Finally, the report generator correlates path connectivity records with policy interception logs. If a path connectivity fails and the error code indicates policy interception (e.g., 403), it is marked as a security policy in effect. If the path failure is due to network timeout or service unreachability, it is marked as an infrastructure failure. For illegal requests missed during policy testing, such as requests carrying SQL injection payloads returning a 200 success response, the vulnerability type (e.g., injection vulnerability), risk level (based on the CVSS scoring framework mapping), and affected service UUID are recorded in the security vulnerability list. The generated deployment verification report uses machine-readable JSON format and contains three core data parts: a path connectivity matrix, a policy interception statistics table, and a security vulnerability list. In the path connectivity matrix, the behavior represents the source service, the column represents the target service, and the value represents the connectivity status code. The policy interception statistics table includes the illegal request type, number of tests, and interception rate. The security vulnerability list includes vulnerability details and remediation suggestions indexed by CWE number.

[0060] Based on the deployment verification report, the environment-aware microservice package is distributed to the target deployment environment node. Specifically, this includes: attaching a digital signature to the microservice package marked as secure in the deployment verification report; generating a distribution routing table based on the environment feature fingerprint corresponding to the target deployment environment node; transmitting the environment-aware microservice package through a bidirectional TLS channel according to the distribution routing table, wherein the payload is encrypted using AES-GCM during transmission; and triggering the container orchestration framework to execute the deployment after the target deployment environment node verifies the integrity of the digital signature and the environment-aware microservice package.

[0061] In one embodiment of this specification, the distribution controller of the deployment server first parses the security assessment results of the deployment verification report, filters the microservice packages marked as "secure" in the report (i.e., the path connectivity test pass rate exceeds a preset threshold and the security vulnerability list is empty), calls the digital certificate management module to attach a digital signature to each secure microservice package, generates an ECDSA private key using a hardware security module (HSM) conforming to the FIPS140-2 standard, performs asymmetric encryption signature on the Docker image digest value (SHA-256 hash) of the microservice package, generates a signature file in PKCS#7 format and writes it to the image metadata layer.

[0062] Subsequently, the routing planning engine loads the environmental feature fingerprints of the target deployment environment nodes, including node IP addresses, availability zone labels, and network topology. Based on the physical distance between nodes (obtained through the cloud provider's metadata API for regional latency data) and bandwidth quotas (such as the maximum throughput of the VPC network flow table), it calculates the optimal transmission path. High-latency nodes (such as transoceanic data centers) are assigned to edge distribution points, and low-latency nodes (within the same availability zone) are grouped into direct-connect groups. A distribution routing table is generated, stored in JSON format, with fields including the target node IP, next-hop gateway address, priority weight, and transmission protocol constraints. The distributor initiates transmissions in the order of the routing table, connects to the target node through a bidirectional TLS channel, and both the client and the server present X.509 certificates in SPIFFE format, forcibly using the TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 suite. During transmission, the microservice packet payload is encrypted with AES-GCM, and the key is derived from the temporary session key negotiated by both parties. A 96-bit random nonce value is added to prevent replay attacks. A GMAC checksum is added for every 128KB data fragment transmitted to ensure data integrity and real-time anti-tampering.

[0063] After the target node receives the data, the verification module performs multiple checks. First, it verifies the validity of the digital signature chain using a pre-set root certificate to confirm that it was issued by a trusted issuing authority. Second, it decrypts the signature file and compares the actual hash value of the image with the declared value within the signature to verify integrity. Finally, it recalculates the image hash and matches it with the original digest value in the report to prevent tampering during transmission. After successful verification, the deployment executor on the node calls the API of the container orchestration framework (such as the kubelet interface of Kubernetes) to submit a deployment task, decompresses the microservice package into a container image and pushes it to the local repository, generates a Pod configuration manifest based on the resource quota declaration, and the Pod configuration manifest includes environment variable injection rules and health check probes. Finally, it schedules and starts the microservice instance on the target node, completing the fully automated secure deployment.

[0064] By dynamically generating adaptation layer code using environmental fingerprints, the system completely eliminates the need for manually written cloud platform-specific logic, enabling a one-time build and seamless multi-cloud deployment. The sandbox environment replicates the production topology at the image level, ensuring test results are consistent with runtime states and effectively improving cross-cloud fault interception rates. Furthermore, a full-chain trust system covering development, testing, and deployment is established, from traffic-layer attack simulation to transport-layer encrypted signature verification. Deployment verification reports are linked to secure distribution, ensuring that vulnerabilities cannot be patched before entering the production environment, meeting the high compliance requirements of financial and government sectors. A quantum routing algorithm optimizes transmission paths based on real-time network metrics, avoiding deployment delays caused by bandwidth contention. The container orchestration framework's automatic scheduling mechanism completes rolling updates within seconds, significantly shortening downtime. By replacing the uncertainty of manual operations with the determinism of machine execution, deployment efficiency is improved while reducing the production accident rate of enterprise applications.

[0065] The technical solutions implemented in this specification fundamentally address the issues of fragmented deployment, weak environment adaptation, and lack of security verification through atomic decoupling of environment topology, cryptographic abstraction of runtime features, and a verification-driven security closed loop. At the environment adaptation level, based on the weighted analysis of application topology using a quantum deconstruction engine, precise decoupling of business logic and infrastructure is achieved. Traditional manually written environment adaptation scripts are transformed into atomically encapsulated high-cohesion business modules, enabling microservice units to possess environment-aware independence. Combined with an encrypted extraction mechanism for environment feature fingerprints, adaptation layer code is dynamically compiled using a rule base, fundamentally eliminating hard-coded dependencies on multi-cloud differences and achieving machine-executable environment configuration. At the security verification level, the sandbox environment mirrors the production topology based on environmental feature fingerprints, enabling the verification of cross-cloud communication link matrices to move from theoretical strategies to physical simulations. By traversing all communication paths using a simulated dataset reconstructed from historical traffic, it exposes dynamic policy gaps that traditional RBAC models cannot cover. The proactive injection test of illegitimate tokens provides runtime stress verification of the zero-trust policy chain, elevating static permission rules to behavioral-level interception capabilities. The resulting deployment verification report becomes a secure and trusted middleware connecting testing and production. In the distribution and deployment phase, the strong binding of digital signatures and verification reports establishes a secure traceability chain for microservice packages at the cryptographic level. The distribution routing table generated based on environmental feature fingerprints essentially compiles network topology policies into a transport control plane, achieving dual trust transfer between the transport and application layers through nested encryption of bidirectional TLS and AES-GCM. Ultimately, when the container orchestration framework is triggered, the environment adaptation layer code has pre-configured multi-cloud compatibility, transforming deployment behavior from manual configuration to declarative scheduling. Atomic decoupling addresses environmental heterogeneity, cryptographic abstraction ensures adaptability determinism, and secure closed-loop verification controls risks before deployment through simulation, interception, and tracing mechanisms, achieving secure deployment throughout the entire process.

[0066] This specification also provides an embodiment of a security deployment device for enterprise-level applications, such as... Figure 2 As shown, the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0067] This specification also provides a non-volatile computer storage medium storing computer-executable instructions configured to execute the above-described method.

[0068] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0069] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A security deployment method for enterprise-level applications, characterized in that, The method includes: Obtain application topology graph data generated by a preset visualization development platform, and then use a quantum deconstruction engine to split the application topology graph data into a set of atomic microservice units to generate a microservice dependency tree; Scan the target deployment environment and extract the environment feature fingerprint of the target deployment environment. Based on the environment feature fingerprint, generate environment adaptation layer code. The environment feature fingerprint includes cloud service provider API type, container orchestration framework version and network configuration strategy. Inject the environment adaptation layer code into each of the atomic microservice units to generate an environment-aware microservice package, and construct a cross-cloud communication link matrix based on the microservice dependency tree; Based on the environment-aware microservice package and the cross-cloud communication link matrix, deployment verification tests are performed in a sandbox environment to generate a deployment verification report. According to the deployment verification report, the environment-aware microservice package is distributed to the target deployment environment nodes. The application topology graph data is split into a set of atomic microservice units using a quantum deconstruction engine, generating a microservice dependency tree, specifically including: The node weights of the application topology graph data are traversed to identify computationally intensive node sets, and the data flow path is cut based on a preset edge weight threshold to aggregate related nodes into highly cohesive business modules. Atomized microservice units are generated for each of the highly cohesive business modules, wherein each atomized microservice unit includes a unique unit identifier, input / output interface specification data, and resource quota declaration information; A microservice dependency tree is generated based on the segmented data flow path, wherein the tree nodes of the microservice dependency tree are atomic microservice units, and the data flow direction and protocol type are marked on the tree side.

2. The security deployment method for enterprise-level applications according to claim 1, characterized in that, Obtain application topology data generated by a preset visualization development platform, specifically including: Receive business process configuration data sent by the visual development platform, wherein the business process configuration data includes component layouts, event binding relationships, and data flow pointers generated by user drag and drop; The business logic engine is invoked to parse the business process configuration data and generate a service call chain diagram. The service call chain graph is merged with the pre-acquired component dependencies to construct weighted application topology graph data, wherein the node weights in the application topology graph data are related to the computational complexity of the components, and the edge weights are related to the data traffic frequency.

3. The security deployment method for enterprise-level applications according to claim 1, characterized in that, Scan the target deployment environment and extract the environmental feature fingerprint of the target deployment environment, specifically including: The encryption probe module sends a TLS 1.3 authentication request to the target environment to obtain the certificate chain and infrastructure metadata returned by the target environment. Extract the cloud service provider's API signature format, container orchestration framework version number, and network ACL rules from the infrastructure metadata; A preset encryption algorithm is used to calculate a digest value for the extracted cloud service provider API signature format, the container orchestration framework version number, and the network ACL rules, generating an immutable environmental feature fingerprint.

4. The security deployment method for enterprise-level applications according to claim 1, characterized in that, Based on the environmental feature fingerprint, an environment adaptation layer code is generated, specifically including: The environmental feature fingerprint is matched with a preset adaptation rule base, wherein the adaptation rule base is used to store cloud service provider SDK mapping relationships; If the identification result is a public cloud environment, then the corresponding cloud provider's API call encapsulation layer and credential rotation module are generated to determine the environment adaptation layer code; If the identification result is a hybrid cloud environment, a cross-cloud service mesh proxy and a two-way certificate authentication module are generated to determine the environment adaptation layer code; The generated environment adaptation layer code is compiled into a dynamic link library, and an environment configuration manifest is attached.

5. A security deployment method for enterprise-level applications according to claim 1, characterized in that, Based on the aforementioned microservice dependency tree, a cross-cloud communication link matrix is ​​constructed, specifically including: Parse the edge attributes of the microservice dependency tree, extract all cross-unit communication paths, and create a communication channel descriptor for each cross-unit communication path, wherein the communication channel descriptor includes source / target unit identifier, transport protocol and QoS level; Based on the network configuration policy in the environmental feature fingerprint, IP whitelist rules and TLS encryption suites are injected into the communication channel descriptor of each cross-unit communication path to determine the current communication channel descriptor; Based on the total number of atomic microservice units in the set of atomic microservice units and the current communication channel descriptor corresponding to each cross-unit communication path, a communication link matrix of a specified dimension is constructed, wherein the matrix elements of the communication link matrix are used to store the current channel descriptor.

6. A security deployment method for enterprise-level applications according to claim 1, characterized in that, Based on the environment-aware microservice package and the cross-cloud communication link matrix, deployment verification tests are performed in a sandbox environment, generating a deployment verification report, which specifically includes: Based on historical business traffic log data, a simulated traffic dataset is determined, and the environment-aware microservice package and the cross-cloud communication link matrix are loaded into an isolation sandbox to inject the simulated traffic dataset to traverse all communication paths in order to determine the path connectivity of each communication path. Send test requests carrying invalid tokens to each microservice unit, check the interception rate, and generate policy interception logs; Based on the path connectivity and the policy-intercepted logs, a deployment verification report containing a list of security vulnerabilities is generated.

7. A security deployment method for enterprise-level applications according to claim 1, characterized in that, Based on the deployment verification report, the environment-aware microservice package is distributed to the target deployment environment nodes, specifically including: Add a digital signature to the microservice packages marked as secure in the deployment verification report; Based on the environmental feature fingerprints corresponding to the target deployment environment nodes, a distribution routing table is generated; According to the distribution routing table, the environment-aware microservice packet is transmitted through a bidirectional TLS channel, wherein the payload is encrypted using AES-GCM during transmission. After verifying the integrity of the digital signature and the environment-aware microservice package on the target deployment environment node, the container orchestration framework is triggered to execute the deployment.

8. A security deployment device for enterprise-level applications, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method as described in any one of claims 1-7.

9. A non-volatile computer storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to perform the method as described in any one of claims 1-7.

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