# What is AI Interrogation?

_AI interrogation encompasses techniques that intentionally query, coax, or stress-test AI systems to find where they fail, leak data, hallucinate, or follow malicious instructions._

AI interrogation encompasses techniques that intentionally query, coax, or stress-test AI systems to find where they fail, leak data, hallucinate, or follow malicious instructions. The aim is defensive: to understand and remediate risks before they become incidents. Best practice treats interrogation as part of a broader [AI supervision](/ai-supervision), [AI safety](/ai-safety) and [AI governance](/ai-governance) program.

- **Attack surface discovery**: Interrogation exposes ways models can be manipulated or coerced into producing harmful or confidential outputs.
- **Regulatory readiness**: Demonstrable adversarial testing supports compliance with emerging AI rules and audit expectations.
- **Security resilience**: Red-teaming simulates real-world threats so teams can strengthen defenses before real attackers exploit them.

## Core AI Interrogation Techniques

- **Prompt-based probing**: Systematic prompts designed to elicit hallucinations, privacy leaks, or policy-violating outputs (e.g., role play, iterative context shifting).
- **Jailbreak & social-engineering attacks**: Use persuasion tactics and contextual tricks to bypass guardrails; research shows many LLMs can be coaxed into risky outputs using carefully framed instructions. See [prompt injection](/ai-prompt-injection) for technical details.
- **AI red-teaming**: Cross-functional adversarial teams (security, ML, product) simulate attackers to find flaws in model design, training data, and runtime behaviors. This is structured, repeatable, and mapped to risk profiles.
- **[Adversarial input generation](/ai-adversarial-testing)**: Algorithmic or human-crafted inputs that force models into edge cases (e.g., obfuscated prompts, data poisoning checks).
- **Socratic & structured questioning**: Iterative, layered questions that reveal internal inconsistencies or hidden assumptions in model outputs.

## Best Practices

- Treat interrogation as continuous, not one-off. Integrate into the model lifecycle.
- Combine automated adversarial generators with human [red teams](/ai-red-teaming) for creative exploit discovery.
- Instrument robust logging and traceability to produce auditable evidence of tests and fixes.
- Prioritize fixes by user impact and data sensitivity; not every failure must be fixed immediately, but critical leaks and malicious outputs do.

## Use Cases & Examples

- **[Customer support assistants](/solutions/customer-experience)**: Interrogate for hallucinations and PII leakage.
- **Decision-support systems**: Test for biased or unsafe reasoning under adversarial framing.
- **Public-facing chatbots**: Simulate social-engineering and persuasion to ensure guardrails hold.

## A Practical Interrogation Framework (5 Steps)

1. **Scope & threat model**: Define assets, data sensitivity, user scenarios, and attacker profiles.
2. **Design tests**: Create prompt suites (normal, edge, adversarial), scenario playbooks, and red-team tasks.
3. **Execute & log**: Run tests in controlled environments; capture full traces, prompts, and outputs for analysis.
4. **Triage & remediate**: Categorize failures (safety, privacy, security, hallucination) and apply fixes (prompting constraints, filtering, model updates).
5. **Close the loop**: Re-run tests, monitor in production, and integrate findings into governance and CI/CD pipelines.

Run structured interrogation campaigns with [Swept AI Evaluate](/product/evaluate).