Securing AI systems / Pamela Isom.

Format:

Book

Author/Creator:

Isom, Pamela, author.

Language:

English

Subjects (All):

Artificial intelligence.

Natural language generation (Computer science).

Computer security.

Physical Description:

1 online resource (44 pages)

Edition:

[First edition].

Place of Publication:

Santa Rosa, CA : O'Reilly Media, Inc., [2026]

Summary:

Enterprises are adopting AI faster than they can secure it. LLMs and browser-based generative AI tools have become part of how business gets done, and sensitive data is flowing through systems that existing security controls were never designed to see. The result is a growing risk of data exposure, security incidents, and significant financial loss. Securing AI Systems gives CISOs and security leaders a tactical and strategic playbook for this challenge. You'll learn why traditional data loss prevention (DLP) falls short for AI workflows and what a modern data-centric approach looks like. The report covers the five pillars of enterprise AI security: discovering shadow AI, understanding data lineage, defining AI-aware policies, enforcing controls at the point of use, and monitoring continuously for risks ranging from data exposure to agentic AI threats. You'll also get a concrete roadmap for implementing a program that protects mission-critical assets, meets regulatory requirements, and keeps the business innovating safely. Understand the security risks introduced by LLMs and generative AI Learn the five pillars of enterprise AI security Identify the limitations of traditional DLP approaches Build AI-aware controls that reduce vulnerabilities and data risk.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1597453011