The Enforcement Logic: Intent, Evidence, Cure, and Penalties
Once a TRAIGA provision appears relevant, the next practical question is procedural: who can act, what can they ask for, and what happens before a penalty is imposed? This chapter traces the Act's enforcement lifecycle—from a complaint or discovery, through an Attorney General investigation and notice, to cure, litigation, or a statutory protection. The point is to understand why evidence of how a system is built, deployed, tested, and corrected can matter even though TRAIGA does not impose one universal assessment filing on every AI system.
The process has a distinctive center of gravity. Chapter 552 puts the Texas Attorney General in the lead, gives the office investigative tools, allows a defined opportunity to cure, and differentiates penalties by the character and duration of the violation. It also limits private lawsuits under the chapter and places additional state-agency sanctions downstream from an Attorney General finding and recommendation. That arrangement makes institutional route and procedural timing part of the substance of the law.
From complaint to investigative demand
TRAIGA gives the Attorney General exclusive authority to enforce Chapter 552, except for the separate pathway that allows a state agency to impose additional sanctions under Section 552.106. The chapter does not provide a private right of action. In practical terms, a person who believes an AI practice violates Chapter 552 does not receive, from this chapter alone, a license to bring a private damages action. The statute instead requires the Attorney General to create and maintain an online mechanism through which a consumer may submit a complaint.
That does not make a complaint insignificant. If the Attorney General receives a complaint alleging a violation, the office may issue a civil investigative demand to determine whether a violation occurred. A civil investigative demand is an investigative request backed by statutory authority; it is not itself a finding of liability. Its function is to move from an allegation to information about the system and the conduct at issue.
The information TRAIGA identifies is revealing because it describes the questions an investigator may ask without requiring a technical code audit. The Attorney General may request a high-level description of the system's purpose, intended use, deployment context, and associated benefits. The office may ask what type of data was used to program or train the system, what categories of data the system processes as inputs, and what outputs it produces. It may request performance metrics and known limitations. It may also seek a high-level account of post-deployment monitoring and user safeguards, including, for a deployer, the oversight, use, and learning process established to address issues arising from deployment.
These requests form an evidence chain. Purpose and intended use bear on the intent and conduct boundaries discussed earlier. Training and input data can help explain what the system was designed to recognize or generate. Outputs and performance metrics connect the system's design to its operation. Limitations, monitoring, safeguards, and oversight show whether the organization had a way to notice and respond to problems after deployment. The statute allows the Attorney General to request high-level information, not necessarily every internal artifact, but a high-level request can still expose gaps in ownership and documentation.
That is an important difference between a statutory information demand and a universal pre-deployment assessment. TRAIGA does not say that every private actor must file all of these materials before putting an AI system into service. It does say that, once an allegation triggers an investigation, the organization's ability to explain the system and its controls may become central. The compliance question is therefore not only “Did we complete the required form?” It is also “Can we give a credible account of purpose, data, outputs, limits, monitoring, and response?”
The same distinction applies to the role of sector agencies. A state agency may sanction a person licensed, registered, or certified by that agency for a Subchapter B violation only if two conditions are met: the person has already been found in violation under the Attorney General's civil-enforcement provision, and the Attorney General has recommended additional enforcement by the applicable agency. Possible sanctions include suspension, probation, or revocation of a license, registration, certificate, or other authorization, as well as a monetary penalty of up to $100,000.
The sequence matters. It makes the Attorney General the primary Chapter 552 gatekeeper and gives a sector regulator a conditional second-stage role. That is not the same as saying sector regulators can never act under any other authority; the supplied statute simply defines this additional-sanctions pathway and its prerequisites. Nor does “exclusive authority” erase laws outside Chapter 552. It describes the enforcement allocation within the chapter.
The process can be represented as a set of transitions rather than a list of remedies:
The starting point is not necessarily a consumer complaint in every imaginable enforcement situation, but the statute specifically ties its investigative-demand mechanism to a complaint received through the required online process. The diagram therefore shows the complaint path that the supplied text expressly describes. It also shows that a reporter's route is not the same as a private lawsuit: the complaint enters an Attorney General process.
Cure, penalties, and evidence of care
If the Attorney General determines that a person has violated or is violating Chapter 552, the office must provide written notice identifying the specific provisions alleged to have been violated. The statute then imposes a timing constraint: the Attorney General may not bring an action before the 60th day after providing notice. A person can also avoid the action on the stated cure pathway if, before that day, they cure the identified violation and provide a written statement confirming three things: that the alleged violation was cured, that supporting documentation shows how it was cured, and that necessary internal-policy changes were made to reasonably prevent a further violation.
This is more than a grace period in the colloquial sense. It gives remediation a defined legal role. A company that discovers a problem after notice must not only change the immediate behavior; it must be able to document what changed and address the organizational conditions that could allow the same violation to recur. The cure statement creates a durable record, and the statute treats a breach of that statement as its own category for penalty purposes.
Cure does not mean every violation is easily repairable. Section 552.105 separates three kinds of monetary exposure. For each violation a court determines to be curable, or for a breach of a cure statement, the penalty is between $10,000 and $12,000. For each violation the court determines to be uncurable, the range is $80,000 to $200,000. A continued violation can add between $2,000 and $40,000 for each day it continues. The Attorney General may also seek an injunction against further violations and recover attorney's fees, reasonable court costs, and other investigative expenses.
These ranges should not be turned into a forecast for a particular organization. The court determines whether a violation is curable or uncurable, and the supplied materials do not show how a court will apply those categories. The useful insight is structural: the law prices persistence and irreversibility differently from a problem that can be corrected and documented. A system that remains in prohibited operation after notice presents a different procedural and financial posture from one whose operator can demonstrate a timely, credible cure.
The statute also creates a rebuttable presumption that a person used reasonable care as required under the chapter. A rebuttable presumption is a starting legal assumption that can be challenged with contrary evidence; it is not an absolute defense. TRAIGA then identifies circumstances in which a defendant may not be found liable. One is another person's use of the AI system in a manner prohibited by the chapter. Others concern how the defendant discovers the violation: feedback from a developer, deployer, or another person who believes a violation occurred; testing, including adversarial or red-team testing; following applicable state-agency guidelines; or an internal review process coupled with substantial compliance with the latest NIST “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile” or another nationally or internationally recognized AI risk-management framework.
The wording rewards detection and response, but it does not eliminate the need to examine the statutory conditions. Substantial compliance is not defined in the supplied material. Nor does a framework citation automatically prove that an organization used an effective process, that the relevant system was covered, or that every violation is excused. The protection is tied to how the violation was discovered and, in the framework case, to an internal review process plus substantial compliance. Treating a risk-management framework as a certificate of immunity would reverse the logic of the provision.
The third-party-use condition has a similar boundary. If another person uses a system affiliated with the defendant in a manner prohibited by Chapter 552, the defendant may not be found liable on that basis. That does not mean a developer or deployer can ignore foreseeable misuse, omit safeguards, or avoid all responsibility for their own conduct. It means the supplied statutory condition distinguishes the defendant's conduct from another person's prohibited use. The statute's investigative-demand categories—purpose, deployment context, safeguards, monitoring, and oversight—are the kinds of facts that could matter when those lines are tested.
The timing rule for undeployed systems provides another important limit. The Attorney General may not bring an action to collect a civil penalty under Section 552.105 against a person for an AI system that has not been deployed. This is not a declaration that pre-deployment conduct is irrelevant under every provision of law. It is a limit on bringing this civil-penalty action for a system that has not entered deployment. That distinction fits TRAIGA's broader architecture: some obligations attach to development, some to deployment, and the enforcement remedy has its own procedural boundary.
The result is a compliance model built around defensible evidence. A developer or deployer needs to know what the system is intended to do, but also what it actually does, what limitations are known, who monitors it, how users are protected, and how reports of harm are handled. Testing is valuable not because it guarantees a clean system, but because the statute expressly recognizes testing and certain review practices when a violation is discovered. Documentation is valuable not because paperwork replaces conduct, but because cure, reasonable care, and statutory protections all depend on facts that can be shown.
That combination also reveals TRAIGA's trade-off. Centralized enforcement can provide a single institutional route and a clear cure process, but a person affected by a system cannot rely on this chapter's private right of action. Intent-based prohibitions can focus the statute on deliberate misuse, but they leave questions about how intent will be inferred in difficult cases. Penalty ranges can create leverage, while the cure structure rewards early remediation. Risk-management evidence can help a defendant, while the meaning of “substantial compliance” awaits further interpretation in the materials available here.
The enforcement lifecycle is therefore not a simple march from complaint to fine. It is a set of branches: allegation to demand, demand to explanation, notice to cure or litigation, and evidence of testing or third-party misuse to a possible statutory protection. Once that machinery is understood, TRAIGA's innovation provisions come into clearer focus. The Act is not only specifying what the Attorney General can do after a problem; it is also creating a supervised setting in which an AI system can be tested while information about performance, mitigation, and stakeholder feedback flows to state institutions.