10-RFP-26-00336 AI Security Questionnaire.xlsx

XLSX spreadsheet 53 KB Posted Jul 9, 2026

Attached to: Integrated Technical Resources Hub
Solicitation number: RFP-26-00336
Issued by: Hillsborough County, Florida

About this file

Written by GovTribe AI · Jul 23, 2026

This is an AI Vendor Security Questionnaire developed by Hillsborough County, Florida for evaluating vendors proposing solutions for an Integrated Technical Resources Hub project. The questionnaire is structured across eight primary categories—Document Requests, Asset Management, Governance, Life Cycle Management, Data and Model Integrity, AI Security, Privacy, and AI Incident Management—with a total of 93 detailed questions designed to assess the security, compliance, and operational controls of AI-based solutions. The County seeks vendors who can demonstrate alignment with industry standards including NIST AI Risk Management Framework (AI RMF), OWASP guidelines for AI/LLM applications, and FedRAMP certifications where applicable. The questionnaire requires vendors to provide supporting documentation such as SOC 2 audit reports, penetration test results, data flow diagrams, model cards, and evidence of security controls across the entire AI system lifecycle.

The questionnaire emphasizes critical security and governance requirements including data classification schemes, isolation of County operational data from training data, detection mechanisms for adversarial data poisoning, incident response procedures specific to AI threats, and transparency controls for automated decision-making. Vendors must address how they protect sensitive information from disclosure and model theft, implement authentication and authorization controls, conduct bias testing and mitigation, maintain audit trails, and monitor for model drift or degradation. Responses are categorized as "Yes," "No," "Partial (Please explain)," or "Not applicable (Please explain)," with vendors required to attach relevant documentation directly into the response form. The questionnaire was initially established in version 1.0 on March 16, 2026, and updated to version 1.1 on May 5, 2026, with revisions focused on removing policy jargon and requiring more detailed explanations beyond simple yes/no answers.

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1-Drug Free Workplace Form.doc.doc	DOC document	Jul 9, 2026
9-RFP-26-00336 Integrated Technical Resources Hub.pdf	PDF	Jul 9, 2026
7-RFP-26-00336 Vendor Security Software Questionnaire.xlsx	XLSX spreadsheet	Jul 9, 2026

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AI Questionnaire_v1.1

AI Vendor Security Questionnaire
Vendor to complete vendor identification section and then respond to all questions. If documents are requested, please use "Insert object" to attach the document directly into the Vendor Comments cell.
	Vendor Name:	e.g. ACME Inc.
	Completed By:	Name of person responding
	Date Completed:

#	Question	Vendor Response	Vendor Comments
1	Document Requests
1.2	Please attach a copy of any relevant audit reports that cover information security controls (e.g. SOC 2).
1.3	Please attach a copy of your latest penetration test and/or vulnerability assessment report.
1.4	Provide a data flow diagram that illustrates the end-to-end lifecycle of County data. This must include how data is ingested, where it is processed (including any third-party AI sub-processors like OpenAI), and where it is stored at rest. Additionally, specify how long data remains 'in memory' during AI inference and your process for purging it once the task is complete.
1.5	Please attach copies of Model Cards or Transparency Cards. If you don't have them, explain why?
2	Asset Management
2.1	Do you maintain an inventory of all hardware and software,AI/ML, and data(training,operational or other assets, including ownership?
2.2	Do you have an information classification scheme and process designed to ensure that information is protected according to its confidentiality requirements?
2.3	How does your system distinguish between and protect different types of data? Specifically, describe how you ensure the County’s live operational data is kept separate from any data used to train or improve your AI models.
2.4	Describe your data classification schema for this AI system. How is each category of data (training, input, output, and metadata) secured to prevent unauthorized disclosure or co-mingling with other customers' data?
2.5	Do you adhere to OWASP guidelines for application security, including the OWASP Top 10 for AI/LLM applications?
2.6	Are AI-specific gaps identified and documented?
2.7	Are parameter values tailored for AI system risks?
3	Governance
3.1	Have you documented your AI risk management approach in alignment with NIST AI RMF core functions (Govern, Map, Measure, Manage)?
3.2	Can you provide evidence of controls for privacy, security, and resilience as per NIST AI RMF guidance?
3.3	Do you have a process for reviewing your information security policy?
3.4	How do you define and measure 'fairness/transparency' for your product/tool, and who in your organization is ultimately accountable if the AI produces biased or inaccurate results? Describe the process for a county manager to review the 'transparency logs' to verify that an automated decision was made correct.
3.5	Have you designated an individual, who is at least at a manager level, who is responsible for information security activities?
3.6	Do you have a process designed to monitor changes to regulations and ensure compliance with relevant security requirements?
3.8	How do you ensure that the data our staff uploads or interacts with hasn't been tampered with or 'poisoned' to give us false results?

Describe the alerts or safeguards that notify a county manager if the AI starts producing inconsistent or suspicious outputs.

3.9	Is model versioning documented with traceability of updates?
3.10	Are availability requirements defined for the AI system?
3.11	Are backup/restore procedures for model artifacts implemented and tested?
3.12	Is the AI system explainable and transparent to stakeholders?
3.13	Are safeguards in place for bias, fairness, and harmful output?
3.14	Describe processes for bias testing and mitigation in AI models
3.15	Are adverse events or misuse incidents tracked?
3.16	Are mechanisms in place to detect model drift or degradation?
4	Life Cycle Management
4.1	Is an AI system lifecycle defined from design to decommissioning?
4.2	Has AI ownership and accountability been defined across the lifecycle?
4.3	Have you implemented controls aligned with the NIST AI Risk Management Framework (AI RMF) for managing AI-related risks across the lifecycle?
4.4	Are prompt restrictions or output filtering mechanisms implemented?
4.5	For agent systems, is supervision and oversight defined?
4.6	Are periodic performance and security reviews conducted?
4.7	Are AI system logs and audit trails maintained?
5	Data and Model Integrity
5.1	Do any AI components train on Hillsborough County data? If yes, describe isolation and consent controls.
5.2	Where is AI training performed (region, cloud provider)?
5.4	Are training data sources validated for accuracy, integrity, and provenance?
5.5	Are controls in place to detect adversarial data poisoning or manipulation?
5.6	Do you mitigate prompt injection and insecure output handling in your AI applications? Please explain how?
5.7	Is model versioning documented with traceability of updates?
6	AI Security
6.1	Does the AI system fall into a high‑risk category
6.2	Does your information security policies include content on AI security?
6.3	Are security measures implemented to safeguard AI systems from external threats, such as cyberattacks or unauthorized access?
6.4	Does your AI system have a FedRAMP instance/environment with the necessary security standards, that provides a framework for agencies/local governments to determine if the cloud service falls within the required FedRAMP level? What impact level is it certified?
6.5	Are you outsourcing any AI processing to a 3rd party? If yes, do you have an agreement that holds them accountable for the data protection?
6.6	Does your incident response plan include AI incidents in their scope?
6.7	Do you perform periodic testing on your AI system/components that includes but is not limited to penetration testing and red-teaming?
6.8	Do you implement input validation and sanitization measures to ensure user input adheres to defined limits and filters out malicious content?
6.9	Do you have appropriate authentication practices in place, such as OAuth 2.0, to implement effective authorization and access control to AI systems?
6.10	Do you enforce privilege control on LLM access to backend systems and follow the principle of least privilege by restricting the LLM to the minimum level of access required for its intended use?
6.13	Do you enforce containerization on all mobile devices that may contain organizational data, including email, whether those devices are owned by the organization or by employees?
6.14	Do you have the capability of deleting all organizational data from mobile devices, whether owned by the organization or by employees, in the event that the device is lost or stolen?
6.15	Does the AI system process sensitive data and is it properly protected? If yes, how do you protect against sensitive information disclosure and model theft?
6.16	Are outputs reviewed for unintended disclosure of sensitive information?
6.17	Are model weights and configurations encrypted at rest and in transit?
6.18	Are third-party AI components subject to contractual cybersecurity requirements?
6.19	Are third-party model dependencies assessed for vulnerabilities?
6.20	Are vendor updates tested for security impact?
6.22	Is access to model artifacts restricted by authentication and authorization?
6.23	Is the AI deployment environment secured (patching, segmentation, monitoring)?
7	Privacy
7.1	Do you have a data retention policy and process that is designed to meet relevant privacy regulations?
7.2	Do you maintain an inventory and mapping of where all personal data is stored that includes cross-border data flows?
8	AI Incident Management
8.1	Are security configuration baselines defined and implemented for all endpoints and network devices?
8.2	Do you segregate your network into zones based on trust levels and control the flow of traffic between zones?
8.3	Does your incident response plan include AI-specific threats (prompt injection, model compromise)
8.4	How does your incident response playbook specifically address adverse events?

Provide evidence of how you classify, track, and report unauthorized use of personal data or synthetic media misuse to ensure the County meets its citizen's obligations.

8.5	Are all AI security incidents recorded, classified, and tracked?
8.6	Are forensic investigations conducted as part of incident response?
8.7	Have you deployed automated tools to collect, correlate, and analyze security event logs from multiple sources for anomalies?
8.8	Are security alerts monitored 24/7?
8.9	Are interactions via APIs or prompts monitored for misuse?

Change Log

Version	Date	Updated By	Change Summary	Notes
v1.0	3/16/26	Michael Farr	Initial version block added; no question content changed.	Baseline established.
v1.1	5/5/26	Michael Farr	Updated questions: 1.4, 3.4, 3.8,8.4	Policy jargon removed. More detailed questions. Moving away from yes/no answers.

Baseline

AI Vendor Security Questionnaire
Vendor to complete vendor identification section and then respond to all questions. If documents are requested, please use "Insert object" to attach the document directly into the Vendor Comments cell.
	Vendor Name:	e.g. ACME Inc.
	Completed By:	Name of person responding
	Date Completed:

#	Question	Vendor Response	Vendor Comments
1	Document Requests
1.2	Please attach a copy of any relevant audit reports that cover information security controls (e.g. SOC 2).	Attached
1.3	Please attach a copy of your latest penetration test and/or vulnerability assessment report.	Attached
1.4	Please provide AI architecture diagram illustrating end-to-end AI workflows, including data ingestion, processing, model interaction, and output layers, with emphasis on data flow, governance, and security controls.	Attached
1.5	Please attach copies of Model Cards or Transparency Cards. If you don't have them, explain why?	Attached
2	Asset Management
2.1	Do you maintain an inventory of all hardware and software,AI/ML, and data(training,operational or other assets, including ownership?	Partial (Please explain)
2.2	Do you have an information classification scheme and process designed to ensure that information is protected according to its confidentiality requirements?	Yes
2.3	Do you maintain an inventory or map of data flows between both internal and external information systems?	No
2.4	Do you classify each data type (training, operational or other data) used by the AI systems	Yes
2.5	Do you adhere to OWASP guidelines for application security, including the OWASP Top 10 for AI/LLM applications?
2.6	Are AI-specific gaps identified and documented?
2.7	Are parameter values tailored for AI system risks?
3	Governance
3.1	Have you documented your AI risk management approach in alignment with NIST AI RMF core functions (Govern, Map, Measure, Manage)?	Yes
3.2	Can you provide evidence of controls for privacy, security, and resilience as per NIST AI RMF guidance?	Not applicable (Please explain)
3.3	Do you have a process for reviewing your information security policy?	Partial (Please explain)
3.4	Do you conduct regular risk assessments for fairness, transparency, and accountability as outlined in NIST AI RMF?	Partial (Please explain)
3.5	Have you designated an individual, who is at least at a manager level, who is responsible for information security activities?	No
3.6	Do you have a process designed to monitor changes to regulations and ensure compliance with relevant security requirements?	Yes
3.8	Are controls in place to detect adversarial data poisoning or manipulation?
3.9	Is model versioning documented with traceability of updates?
3.10	Are availability requirements defined for the AI system?
3.11	Are backup/restore procedures for model artifacts implemented and tested?	Partial (Please explain)
3.12	Is the AI system explainable and transparent to stakeholders?
3.13	Are safeguards in place for bias, fairness, and harmful output?
3.14	Describe processes for bias testing and mitigation in AI models
3.15	Are adverse events or misuse incidents tracked?
3.16	Are mechanisms in place to detect model drift or degradation?
4	Life Cycle Management
4.1	Is an AI system lifecycle defined from design to decommissioning?
4.2	Has AI ownership and accountability been defined across the lifecycle?
4.3	Have you implemented controls aligned with the NIST AI Risk Management Framework (AI RMF) for managing AI-related risks across the lifecycle?
4.4	Are prompt restrictions or output filtering mechanisms implemented?
4.5	For agent systems, is supervision and oversight defined?
4.6	Are periodic performance and security reviews conducted?
4.7	Are AI system logs and audit trails maintained?
5	Data and Model Integrity
5.1	Do any AI components train on Hillsborough County data? If yes, describe isolation and consent controls.”
5.2	Where is AI training performed (region, cloud provider)?
5.4	Are training data sources validated for accuracy, integrity, and provenance?
5.5	Are controls in place to detect adversarial data poisoning or manipulation?
5.6	Do you mitigate prompt injection and insecure output handling in your AI applications (OWASP LLM01 & LLM02)? Please explain
5.7	Is model versioning documented with traceability of updates?
6	AI Security
6.1	Does the AI system fall into a high‑risk category
6.2	Does your information security policies include content on AI security?
6.3	Are security measures implemented to safeguard AI systems from external threats, such as cyberattacks or unauthorized access?
6.4	Does your AI system have a FedRAMP instance/environment with the necessary security standards, that provides a framework for agencies/local governments to determine if the cloud service falls within the required FedRAMP level? What impact level is it certified? Level(1-6)
6.5	Are you outsourcing any AI processing to a 3rd party? If yes, do you have an agreement that holds them accountable for the data protection?
6.6	Does your incident response plan include AI incidents in their scope?
6.7	Do you perform periodic testing on your AI system/components that includes but is not limited to penetration testing and red-teaming?
6.8	Do you implement input validation and sanitization measures to ensure user input adheres to defined limits and filters out malicious content?
6.9	Do you have appropriate authentication practices in place, such as OAuth 2.0, to implement effective authorization and access control to AI systems?
6.10	Do you enforce privilege control on LLM access to backend systems and follow the principle of least privilege by restricting the LLM to the minimum level of access required for its intended use?
6.13	Do you enforce containerization on all mobile devices that may contain organizational data, including email, whether those devices are owned by the organization or by employees?
6.14	Do you have the capability of deleting all organizational data from mobile devices, whether owned by the organization or by employees, in the event that the device is lost or stolen?
6.15	Does the AI system process sensitive data and is it properly protected? If yes, how do you protect against sensitive information disclosure and model theft (OWASP LLM06 & LLM10)?
6.16	Are outputs reviewed for unintended disclosure of sensitive information?
6.17	Are model weights and configurations encrypted at rest and in transit?
6.18	Are third-party AI components subject to contractual cybersecurity requirements?
6.19	Are third-party model dependencies assessed for vulnerabilities?
6.20	Are vendor updates tested for security impact?
6.22	Is access to model artifacts restricted by authentication and authorization?
6.23	Is the AI deployment environment secured (patching, segmentation, monitoring)?
7	Privacy
7.1	Do you have a data retention policy and process that is designed to meet relevant privacy regulations?
7.2	Do you maintain an inventory and mapping of where all personal data is stored that includes cross-border data flows?
8	AI Incident Management
8.1	Are security configuration baselines defined and implemented for all endpoints and network devices?
8.2	Do you segregate your network into zones based on trust levels and control the flow of traffic between zones?
8.3	Does your incident response plan include AI-specific threats (prompt injection, model compromise)? Provide playbook excerpts.
8.4	Are all AI security incidents recorded, classified, and tracked?
8.5	Are forensic investigations conducted as part of incident response?
8.6	Have you deployed automated tools to collect, correlate, and analyze security event logs from multiple sources for anomalies?
8.7	Are security alerts monitored 24/7?
8.8	Are interactions via APIs or prompts monitored for misuse?

3. Lists (Hidden)

Data Entry Lists

This tab controls the lists that are used for the data entry tab. Hide this tab when not in use.

Valid responses for document request questions

Attached

Not attached (Please explain)

Not applicable

Valid responses for all other questions

Partial (Please explain)

Yes

Not applicable (Please explain)

File details come from the government source that posted it. Updated Jul 23, 2026.