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Trustworthy AI. Red Teaming, Runtime Assurance, and the Architecture of Secure Intelligence Sandeep Shilawat

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Trustworthy AI. Red Teaming, Runtime Assurance, and the Architecture of Secure Intelligence Sandeep Shilawat - okladka książki

Trustworthy AI. Red Teaming, Runtime Assurance, and the Architecture of Secure Intelligence Sandeep Shilawat - okladka książki

Trustworthy AI. Red Teaming, Runtime Assurance, and the Architecture of Secure Intelligence Sandeep Shilawat - audiobook MP3

Trustworthy AI. Red Teaming, Runtime Assurance, and the Architecture of Secure Intelligence Sandeep Shilawat - audiobook CD

Autor:
Sandeep Shilawat
Ocena:
Stron:
170
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AI is moving from generating answers to making decisions and taking action, yet many organizations still rely on alignment claims, safety filters, and one-time tests to decide whether systems can be trusted. Trustworthy AI argues that assurance must be measurable, continuous, and enforced at runtime.

The book begins with how language-based systems fail under pressure: prompt injection, persona jailbreaks, system-prompt leakage, multi-turn Crescendo escalation, and bias. A red-team case study of a sovereign AI model shows why AI risk is not simply safe or failed, but often exists in a dangerous middle.

From there, the book moves from evidence to architecture. Sandeep Tulshiram Shilawat introduces the TIVM framework for quantifying AI risk and turning it into actionable assurance signals. He then presents an operating model built on continuous red teaming, AI observability, runtime guardrails, red-team agents, and independent AI judge systems. Final chapters connect these controls to evidence-based compliance, regulated and sovereign environments, zero-trust architecture, and Project NANDA.

For executives, security leaders, architects, engineers, regulators, and mission owners, this book provides a practical path from trusting AI by assertion to governing it through continuous evidence.

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