Symbolic containment firewall for audited language

1 . A computer implemented method of enforcing symbolic containment for a natural language transaction, the method comprising:

a. receiving, by an ingress interface, a request comprising input text, optional structured context, and a domain profile identifier;

b. loading, by at least one processor, a domain profile associated with the domain profile identifier, the domain profile specifying required anchors, threshold values, and weighting parameters;

c. normalizing, by the at least one processor, the input text to generate normalized text and extracting, from the normalized text and the structured context, candidate anchors, and setting at least one flag indicating whether required anchors are missing;

d. computing heuristic features that include an entropy score based on high entropy markers in the normalized text, an anchor coverage value that represents how many required anchors are present, and one or more flags that indicate omissions or self contradictions;

e. invoking, by the at least one processor, a model adapter that sends the normalized text and domain cues to a language model or other structural analyzer and receives a model stability score and inconsistency findings;

f. aggregating, by a symbolic containment scoring engine executed by the at least one processor, the heuristic features and the model stability score according to the weighting parameters in the domain profile to produce a containment stability score and a containment stability label;

g. selecting, by a decision engine, based on the containment stability score, the stability label, and the at least one flag, a decision action that includes one of pass, block, or escalate and optionally generating a refactored text that satisfies requirements in the domain profile;

h. constructing, by the at least one processor, an audit object that includes the domain profile identifier, the normalized text, the heuristic features, the model stability score, the containment stability score, the stability label, the decision action, any rationale, and any refactored text;

i. canonicalizing the audit object into a deterministic byte representation, computing a cryptographic seal over the deterministic byte representation using a cryptographic hash function, and embedding the cryptographic seal into the audit object;

j. assigning a record identifier to the audit object and writing the audit object into a write once read many ledger using a conditional write that rejects an attempted overwrite when the record identifier already exists; and

k. controlling a downstream execution system so that modification of persistent state is delayed until after the audit object has been successfully written to the write once read many ledger and so that only requests associated with the decision action of pass are permitted to modify state in the downstream execution system.

2 . The method of claim 1 , wherein the domain profile defines high entropy markers comprising ambiguous terms and patterns, and the entropy score is computed based on occurrences of the high entropy markers relative to a total number of tokens in the normalized text.

3 . The method of claim 1 , wherein the domain profile specifies a minimum pass score, a block score, and one or more escalation thresholds, and the selecting comprises comparing the containment stability score to the minimum pass score, the block score, and the one or more escalation thresholds.

4 . The method of claim 1 , wherein the model adapter abstracts vendor specific language model interfaces and normalizes responses from different providers into a fixed internal schema used by the symbolic containment scoring engine.

5 . The method of claim 1 , further comprising re evaluating the refactored text by repeating the computing, invoking, and aggregating to produce a post refactor containment stability score stored in the audit object.

6 . The method of claim 1 , wherein the method is performed without persisting conversational state between different natural language transactions so that each transaction is evaluated on a self contained transaction object.

7 . The method of claim 1 , wherein the write once read many ledger is implemented using a key value store configured to reject writes that attempt to overwrite a record identifier that already exists.

8 . The method of claim 1 , further comprising training or calibrating the weighting parameters and the threshold values in the domain profile using an adversarial corpus of narratives that include paradoxical, recursive, or deceptive text patterns.

9 . The method of claim 1 , wherein the domain profile encodes required anchors selected from ticket identifiers, case identifiers, customer identifiers, or regulatory citations and further encodes role based escalation rules that route the escalate decision action to a human reviewer.

10 . The method of claim 1 , wherein the ingress interface receives the request from an authoring interface operated by a human or upstream generative model and the downstream execution system comprises at least one of an administrative console, a database, or a publication engine, and the method is executed between the authoring interface and the downstream execution system.

11 . A system for symbolic containment of natural language transactions, comprising:

a. a client interface including at least one input endpoint that receives input text, optional context, and a domain profile identifier from a human user, an application, or a generative model;

b. an ingress application programming interface coupled to the client interface;

c. a domain profile store;

d. a write once read many ledger;

e. a downstream execution system; and

f. at least one processor coupled to a non transitory memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

g. loading, from the domain profile store, a domain profile that provides a domain profile associated with the domain profile identifier;

h. generating normalized text from the input text and extracting candidate anchors;

i. computing heuristic features, invoking model-adapter instructions to obtain a model stability score, and aggregating the heuristic features and the model stability score into a containment stability score and a stability label;

j. selecting a decision action including one of pass, block, or escalate and optionally generating a refactored text;

k. constructing a canonical audit object containing the normalized text, the domain profile identifier, the heuristic features, the model stability score, the containment stability score, the stability label, any rationale, the decision action, and any refactored text, and that computes a cryptographic seal over a canonical representation of the canonical audit object;

l. assigning a record identifier to the canonical audit object and storing the canonical audit object with the cryptographic seal in the write once read many ledger using a conditional write that rejects an attempted overwrite when the record identifier already exists; and

m.

n. delaying modification of persistent state in the downstream execution system until after the canonical audit object has been stored in the write once read many ledger and permitting modification of persistent state only when the decision action is a pass decision.

12 . The system of claim 11 , wherein the non transitory memory further stores model-adapter instructions that, when executed by the at least one processor, cause the at least one processor to communicate with multiple language model providers and normalize responses into a fixed internal schema independent of provider.

13 . The system of claim 11 , wherein the write once read many ledger comprises a cloud hosted database configured with conditional writes that prevent modification of existing records.

14 . The system of claim 11 , further comprising a verification application programming interface having a network endpoint and verification instructions stored in the non transitory memory that, when executed by the at least one processor, cause the at least one processor to retrieve a stored canonical audit object by record identifier, remove or ignore a stored cryptographic seal field, re canonicalize remaining fields using the deterministic canonicalization procedure used to create the canonical representation, recompute the cryptographic seal, compare the recomputed cryptographic seal to the stored cryptographic seal, and output a verification result.

15 . The system of claim 11 , wherein the domain profile store maintains a plurality of domain profiles with different threshold values and anchor requirements for different domains selected from incident reporting, corporate governance communication, and creative writing.

16 . A non transitory computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

a. receiving a request that includes input text, optional context, and a domain profile identifier;

b. retrieving a domain profile associated with the domain profile identifier, the domain profile specifying required anchors, threshold values, and weighting parameters;

c. normalizing the input text to generate normalized text and extracting anchors from the normalized text and the optional context;

d. computing heuristic features and a model assisted structural stability score for the normalized text;

e. aggregating the heuristic features and the model assisted structural stability score according to the weighting parameters to produce a containment stability score and a containment stability label;

f. selecting, based on the containment stability score and any flags defined by the domain profile, a decision action including one of pass, block, or escalate;

g. building a canonical audit object that includes the request, the domain profile identifier, the heuristic features, the containment stability score, the stability label, and the decision action;

h. computing a cryptographic seal over a canonical representation of the canonical audit object, assigning a record identifier to the canonical audit object, and storing the canonical audit object and the cryptographic seal in a write once read many ledger using a conditional write that rejects an attempted overwrite when the record identifier already exists; and

i. outputting the decision action for use by a downstream execution system that delays application of the input text until after successful storage of the canonical audit object in the write once read many ledger and that controls application of the input text so that only pass decisions are applied.

17 . The non transitory computer readable storage medium of claim 16 , wherein the operations further comprise generating a machine readable rationale object that explains contributors to the containment stability score and including the machine readable rationale object in the canonical audit object.

18 . The non transitory computer readable storage medium of claim 16 , wherein the operations further comprise generating a refactored version of the input text that satisfies at least one requirement specified in the domain profile and storing the refactored version in the canonical audit object.

19 . The non transitory computer readable storage medium of claim 16 , wherein the operations further comprise calibrating the threshold values in the domain profile based on evaluation of an adversarial corpus of narratives that are labeled as stable or unstable by human experts.

20 . The non transitory computer readable storage medium of claim 16 , wherein the operations are performed for natural language requests originating from both human operated authoring interfaces and automated agent processes and the canonical audit object further records an actor identity associated with the request.