AI red-teaming

AI red-teaming is a structured simulation of a malicious actor's actions against your AI solution, broader and more systematic than pointed adversarial testing (906): modeling threat scenarios (bypassing guardrails, prompt injection, data/system-prompt leaks, function abuse, provoking harmful/dangerous output, attacks on agents), assessing consequences for the business, reputation and users, prioritizing risks and recommendations. Honestly about the fundamental boundary, this is key: red-teaming can FIND risks but not prove their absence. 'The team could not break it' does not mean 'it is impossible to break' — the absence of found holes does not equal their absence (a basic security principle). We honestly report coverage and findings rather than pass off 'passed red-team' as 'the AI is safe'. Whoever promises 'full safety as a result' is misleading. Honestly about scope: it is impossible to check all conceivable attacks; we cover priority scenarios realistic for your context and honestly state what was left out. Honestly about evolution: AI attack techniques develop fast, so a one-off red-team becomes outdated — regularity is needed, especially after changes. Honestly about the link: red-teaming reveals, while guardrails (905) and refinement eliminate risks; the 'find → close → recheck' cycle. A report without implementing fixes does not raise security. Honestly about the effect: it substantially reduces the risk of serious incidents by revealing weak spots before attackers, but it is risk management, not elimination. Honestly about ethics: only your AI, with permission, responsibly. Honestly about access: access to the AI application and risk context are needed. An important boundary: this is red-teaming (broader); pointed testing — 906; elimination — guardrails 905; bias — 909. Picture this: instead of 'hoping it will be fine' — a systematic risk check and a plan to close them, regularly. The base price starts from 65,000 ₽ (depends on depth and scope).

• AI security was not checked systematically, only 'by luck'.
• The real AI risks for the business and users are unknown.
• Guardrails and protection are not tested by a comprehensive attack.
• After AI changes security is not reassessed.

• Systematic simulation of attacks on AI (broader than pointed test 906)
• Modeling threat scenarios for your context
• Assessing consequences for business/reputation/users
• A prioritized risk report and recommendations
• Honest boundaries (we find but do not prove safety; limited scope)
• Indicating regularity (threats evolve)
• A cycle with elimination (guardrails 905) and rechecking
• Handover and review with you

• Systematically revealed AI risks before attackers
• Priorities and a risk-reduction plan
• Assessment of business consequences, not only technical
• Honest boundaries (risk reduction, not a safety guarantee)

• We define the threat model and scope; collect access and context
• We systematically attack the AI, assess consequences, prioritize
• We give a report and fix plan, honestly set boundaries (regularity) with you

• After red-teaming will my AI be safe?

  Safer — yes; fully safe — no, and that is honest. Red-teaming finds and prioritizes risks but does not prove their absence: 'could not break it' does not equal 'cannot be broken'. It is impossible to check all attacks, and threats evolve. We honestly report coverage and findings. Whoever promises 'full safety' as a result is misleading.

• How is red-teaming different from adversarial testing?

  Adversarial testing (906) is a more pointed check for specific bypasses. Red-teaming is broader and more systematic: it models real threat scenarios, assesses consequences for the business and users, not only technical holes. They are often combined. We will honestly suggest the needed depth for your risk profile rather than sell 'the most expensive'.

• Will a red-team report make the AI protected?

  No, honestly: a report reveals risks but does not eliminate them. Security rises when what is found is closed — that is guardrails (905) and refinement, the 'find → close → recheck' cycle. A red-team without implementing fixes is knowledge of holes but not their closing. We honestly show the whole cycle and can run it or hand the report to your team.