Method and system for migrating web application firewall (WAF) configuration data across WAF providers

1 . A method for migrating web application firewall (WAF) rules across different WAF providers, comprising:

parsing a plurality of WAF rules from a plurality of WAF providers, wherein the plurality of WAF rules is expressed in varying provider-specific formats;

enriching a source WAF rule of a source WAF provider of the plurality of WAF providers with organizational context derived from a plurality of signals from a tenant computing environment;

generating, using a trained cross-provider semantic similarity model, a provider-agnostic language representation of the source WAF rule based on the provider-specific format of the source WAF rule and the organizational context;

constructing a capability model of a target WAF provider of the plurality of WAF providers including at least rule types, features, and constraints supported by a target WAF provider;

generating, using the trained cross-provider semantic similarity model, a target WAF rule for deployment in the target WAF provider based on the provider-agnostic language representation of the source WAF rule and compatible with the capability model of the target WAF provider; and

coordinating a staged deployment of the target WAF rule in the target WAF provider.

2 . The method of claim 1 , generating the target WAF rule further comprises:

generating a candidate WAF rule for deployment in the target WAF provider based on the provider-agnostic language representation of the source WAF rule and the capability model of the target WAF provider;

generating a first vector representation, in a vector database, of the provider-agnostic language representation of the source WAF rule;

generating a candidate vector representation, in the vector database, of the candidate WAF rule;

computing a distance in the vector database between the first vector representation and the candidate vector representation; and

determining the candidate WAF rule as the target WAF rule when the computed distance is below a predetermined distance threshold.

3 . The method of claim 1 , further comprising:

detecting a security intent of the source WAF rule based on the provider-agnostic language representation;

determining, using the capability model of the target WAF provider, when the security intent can be implemented in the target WAF rule with reduced complexity from the source WAF rule; and

when the security intent can be implemented in the target WAF rule with reduced complexity from the source WAF rule, generating a reduced-complexity target WAF rule for the target WAF provider.

4 . The method of claim 1 , wherein generating the provider-agnostic language representation further comprises:

encoding parsed logic, conditions, match fields, and actions of the source WAF rule into a vectorized representation using the trained cross-provider semantic similarity model that transforms rule structures into numerical feature vectors;

computing, by the trained cross-provider semantic similarity model, semantic similarity scores between the vectorized representation of the source WAF rule and a plurality of predefined universal security intent representations corresponding to common web application protection objectives; and

selecting, based on the computed semantic similarity scores satisfying a predetermined similarity threshold, a normalized security intent definition that represents an underlying security objective of the source WAF rule independently of vendor-specific syntax.

5 . The method of claim 1 , wherein enriching the source WAF rule with organizational context further comprises:

analyzing the plurality of signals including domain relationships, infrastructure metadata, certificate associations, and traffic characteristics

clustering a plurality of web application assets of the tenant into logical application groupings; and

associating the source WAF rule with at least one clustered web application asset.

6 . The method of claim 5 , wherein clustering a plurality of web application assets of the tenant into logical application groupings further comprises:

assigning weights to the multiple signals;

computing a composite correlation score for a pair of web application assets; and

merging the pair web application assets into a common cluster when the composite correlation score exceeds a predetermined threshold.

7 . The method of claim 1 , further comprising:

detecting, using the trained cross-provider semantic similarity model, a security intent of each WAF rule of the plurality of WAF rules;

correlating security intents of the plurality of WAF rules with the plurality of signals including business logic, observed traffic patterns, and protected application assets of the tenant;

detecting at least one of conflicting rules, redundant rules, ineffective rules, coverage gaps, or rules misaligned with the organizational context.

8 . The method of claim 1 , wherein coordinating the staged deployment further comprises:

maintaining active enforcement of security controls in both the source WAF provider and the target WAF provider during a migration interval, such that protection of protected web application assets is continuously enforced by at least one of the source WAF provider or the target WAF provider at all times.

9 . The method of claim 1 , wherein coordinating the staged deployment further comprises:

prior to deploying the target WAF rule, executing a machine learning model trained on historical traffic telemetry to predict an impact of the target WAF rule on legitimate traffic;

computing at least one risk metric indicative of legitimate traffic disruption based on the prediction; and

adjusting deployment parameters of the staged deployment based on the computed risk metric.

10 . A non-transitory computer-readable medium storing a set of instructions for migrating web application firewall (WAF) rules across different WAF providers, the set of instructions comprising:

one or more instructions that, when executed by one or more processing circuitries of a device, cause the device to:

parse a plurality of WAF rules from a plurality of WAF providers, wherein the plurality of WAF rules is expressed in varying provider-specific formats;

enrich a source WAF rule of a source WAF provider of the plurality of WAF providers with organizational context derived from a plurality of signals from a tenant computing environment;

generate, using a trained cross-provider semantic similarity model, a provider-agnostic language representation of the source WAF rule based on the provider-specific format of the source WAF rule and the organizational context;

construct a capability model of a target WAF provider of the plurality of WAF providers including at least rule types, features, and constraints supported by a target WAF provider;

generate, using the trained cross-provider semantic similarity model, a target WAF rule for deployment in the target WAF provider based on the provider-agnostic language representation of the source WAF rule and compatible with the capability model of the target WAF provider; and

coordinate a staged deployment of the target WAF rule in the target WAF provider.

11 . A system for migrating web application firewall (WAF) rules across different WAF providers comprising:

a processing circuitry;

a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:

parse a plurality of WAF rules from a plurality of WAF providers, wherein the plurality of WAF rules is expressed in varying provider-specific formats;

enrich a source WAF rule of a source WAF provider of the plurality of WAF providers with organizational context derived from a plurality of signals from a tenant computing environment;

generate, using a trained cross-provider semantic similarity model, a provider-agnostic language representation of the source WAF rule based on the provider-specific format of the source WAF rule and the organizational context;

construct a capability model of a target WAF provider of the plurality of WAF providers including at least rule types, features, and constraints supported by a target WAF provider;

generate, using the trained cross-provider semantic similarity model, a target WAF rule for deployment in the target WAF provider based on the provider-agnostic language representation of the source WAF rule and compatible with the capability model of the target WAF provider; and

coordinate a staged deployment of the target WAF rule in the target WAF provider.

12 . The system of claim 11 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

generate the target WAF rule further comprises:

generate a candidate WAF rule for deployment in the target WAF provider based on the provider-agnostic language representation of the source WAF rule and the capability model of the target WAF provider;

generate a first vector representation, in a vector database, of the provider-agnostic language representation of the source WAF rule;

generate a candidate vector representation, in the vector database, of the candidate WAF rule;

compute a distance in the vector database between the first vector representation and the candidate vector representation; and

determine the candidate WAF rule as the target WAF rule when the computed distance is below a predetermined distance threshold.

13 . The system of claim 11 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect a security intent of the source WAF rule based on the provider-agnostic language representation;

determine, using the capability model of the target WAF provider, when the security intent can be implemented in the target WAF rule with reduced complexity from the source WAF rule; and

when the security intent can be implemented in the target WAF rule with reduced complexity from the source WAF rule, generate a reduced-complexity target WAF rule for the target WAF provider.

14 . The system of claim 11 , wherein the memory contains further instructions that, when executed by the processing circuitry for generating the provider-agnostic language representation, further configure the system to:

encode parsed logic, conditions, match fields, and actions of the source WAF rule into a vectorized representation using the trained cross-provider semantic similarity model that transforms rule structures into numerical feature vectors;

compute, by the trained cross-provider semantic similarity model, semantic similarity scores between the vectorized representation of the source WAF rule and a plurality of predefined universal security intent representations corresponding to common web application protection objectives; and

select, based on the computed semantic similarity scores satisfying a predetermined similarity threshold, a normalized security intent definition that represents an underlying security objective of the source WAF rule independently of vendor-specific syntax.

15 . The system of claim 11 , wherein the memory contains further instructions that, when executed by the processing circuitry for enriching the source WAF rule with organizational context, further configure the system to:

analyze the plurality of signals including domain relationships, infrastructure metadata, certificate associations, and traffic characteristics clustering a plurality of web application assets of the tenant into logical application groupings; and

associate the source WAF rule with at least one clustered web application asset.

16 . The system of claim 15 , wherein the memory contains further instructions that, when executed by the processing circuitry for clustering a plurality of web application assets of the tenant into logical application groupings, further configure the system to:

assign weights to the multiple signals;

compute a composite correlation score for a pair of web application assets; and

merge the pair web application assets into a common cluster when the composite correlation score exceeds a predetermined threshold.

17 . The system of claim 11 , wherein the memory contains further instructions which when executed by the processing circuitry further configure the system to:

detect, using the trained cross-provider semantic similarity model, a security intent of each WAF rule of the plurality of WAF rules;

correlate security intents of the plurality of WAF rules with the plurality of signals including business logic, observed traffic patterns, and protected application assets of the tenant; and

detect at least one of conflicting rules, redundant rules, ineffective rules, coverage gaps, or rules misaligned with the organizational context.

18 . The system of claim 11 , wherein the memory contains further instructions that, when executed by the processing circuitry for coordinating the staged deployment, further configure the system to:

maintain active enforcement of security controls in both the source WAF provider and the target WAF provider during a migration interval, such that protection of protected web application assets is continuously enforced by at least one of the source WAF provider or the target WAF provider at all times.

19 . The system of claim 11 , wherein the memory contains further instructions that, when executed by the processing circuitry for coordinating the staged deployment, further configure the system to:

prior to deploying the target WAF rule, execute a machine learning model trained on historical traffic telemetry to predict an impact of the target WAF rule on legitimate traffic;

compute at least one risk metric indicative of legitimate traffic disruption based on the prediction; and

and adjust deployment parameters of the staged deployment based on the computed risk metric.