Red teaming for AI systems in local government

Why (and when) to do red teaming in municipal AI projects

Red teaming is a structured adversarial assessment designed to uncover security, privacy, and robustness failures in AI systems before deployment or during operation. In local government it is especially useful when:

• The system makes decisions that affect citizens' rights (grants, sanctions, tenders).
• The service processes sensitive personal data (case files, supporting documents).
• The service has high public visibility (chatbots, service portals).
• There are regulatory obligations (ENS RD 311/2022, GDPR, EU AI Act).

Conducting red teaming reduces operational risk and helps meet audit and transparency obligations.

What does an AI red teaming exercise cover? (practical view)

An AI red team is not just traditional pentesting. At minimum it should include:

• Technical threats: prompt injection, training data poisoning, adversarial evasion, model extraction.
• Privacy risks: re-identification, leakage of sensitive data, input reconstruction.
• Integrity risks: manipulation of input data (corrections, third-party contributions).
• Procedural and legal risks: incorrect decisions due to bias, lack of traceability for audits.
• Availability and continuity risks: degradation from anomalous loads or DoS-style attacks adapted to the model.

8-week plan: how to structure a first red teaming exercise

Week 0: Governance and authorization

• Obtain formal authorization from the data controller and the competent body (internal audit, legal).
• Classify the system according to ENS and assess whether it falls under “high risk” per the EU AI Act.

Week 1: Scope and objectives definition

• Select concrete functionalities (automatic bid evaluation, citizen service chatbot).
• Define boundaries: test environment, allowed data, duration, stop criteria.

Week 2: Threat modeling and test design

• Map data flows and entry points.
• Create concrete test cases: prompt injection, borderline inputs, malicious data, re-identification attempts.

Week 3–4: Execution in a controlled environment

• Carry out technical attacks in isolated staging (do not use real data unless strictly necessary).
• Simulate functional scenarios with real users (usability and manipulation tests).

Week 5: Results analysis and findings prioritization

• Classify failures by impact and likelihood.
• Produce a mitigation plan with deadlines and responsible parties.

Week 6–7: Remediation and verification

• Implement mitigations (input filters, context limits, enhanced logging).
• Repeat tests on corrected components.

Week 8: Final report and handover

• Deliver a technical report plus an executive summary for management.
• Incorporate findings into the risk register and compliance roadmap.

Concrete examples of test cases

• Automatic bid evaluation: attempts to manipulate scores via unusual fields in documentation (metadata injection).
• Citizen chatbot: prompt injection to make the bot disclose confidential data or perform actions (e.g., modify a case file).
• Grant justification system: corrupted input data that causes wrongful denials or loss of traceability.

Useful metrics and minimum deliverables

• Number of attack vectors identified and remediated.
• Mean time to detection and remediation (MTTR) for critical issues.
• Reproducible tests with scripts and a synthetic dataset.
• Evidence of logs, traces, and changes in version control.
• Executive report covering legal impact (GDPR, transparency obligations, ENS).

Legal and compliance requirements to verify

• GDPR: minimize exposure of personal data during tests; if real data is used, document the legal basis and apply anonymization or pseudonymization techniques.
• ENS RD 311/2022: verify applicable security measures according to the service classification.
• EU AI Act: for systems considered “high risk” (e.g., automated administrative decisions) document robustness tests and mitigate bias risks.
• Law 9/2017 and Law 38/2003: when tests affect procurement or grant processes, coordinate with the technical secretariat to guarantee procedural integrity.

Team composition and competencies

• Red team (adversarial): ML security specialists, pentesters, a privacy expert.
• Blue team (defense): developers, service owner, infra administrator, legal.
• Compliance observer and/or internal auditor to ensure traceability.

Procurement and SLA: what to include in specifications and agreements

• Rights to perform adversarial tests and test windows in a sandbox.
• Provider obligations to fix vulnerabilities within specified timelines.
• Confidentiality clauses and data handling rules for tests.
• Requirements to deliver scripts, synthetic datasets, and mitigation plans.

Call to action (takeaway)

Run an 8-week red teaming pilot for one of your highest-risk services (for example, automatic grant evaluation or a citizen service chatbot). Define the scope, execute controlled adversarial tests, document evidence, and demand contractual commitments for remediation. This process not only improves technical security but also helps satisfy ENS, GDPR, and EU AI Act requirements.

If you need a test plan template or an operational checklist adapted for municipalities, OptimTech can share an example that integrates technical and legal requirements for an initial iteration.