Threat Scenarios

What AIQ CySy protects you from.

Realistic AI-native attack paths across LLMs, agents, pipelines, and human decision-makers.

LLM-Powered Customer Support

Customers interact with an LLM assistant backed by internal data.

  • Retrieval poisoning via compromised knowledge bases
  • Prompt injection to bypass safety and leak data
  • Impersonation and fraud through manipulated responses
  • Regulatory exposure from incorrect or biased answers

AI-Augmented Security Operations

SOC analysts use LLM copilots to summarize alerts and suggest actions.

  • Adversarial prompts shape analyst decisions
  • Alert summaries that hide critical indicators
  • Model drift under targeted adversarial inputs
  • Low-visibility changes to response playbooks

Agentic Workflows & Automation

Autonomous or semi-autonomous agents act across tools and APIs.

  • Unexpected action chains triggered by crafted inputs
  • Agent-to-agent amplification of adversarial goals
  • Shadow decision paths outside of existing controls
  • Difficulty reconstructing what actually happened
How AIQ CySy Responds

From unknown risk to defined, defendable surfaces.

  • Map each scenario to AI→AI, AI→Human, Human→AI, and Environment→AI pathways.
  • Apply zero-day reasoning to identify not-yet-documented risks.
  • Produce a clear attack graph and prioritized mitigation list.
  • Align findings with NIST, ENISA, OWASP GenAI, and MITRE ATLAS.

Outcome

You leave each engagement with a concrete understanding of:

  • Where AI can be attacked or abused
  • What the most likely and most damaging paths are
  • Which controls close the highest-risk gaps
  • How to communicate the plan to leadership and regulators

The goal is not theoretical risk — it is a practical path to defensible AI operations.