Data and Goliath
About this book
Schneier describes the surveillance that falls out of ordinary technology use, covering both government collection and the commercial data economy that supplies it. The first part sets out what gets collected and how, including location history, metadata and the inferences drawn from combining otherwise dull sources. The second part argues about the harms, covering chilling effects, discrimination through algorithmic sorting, and the security weaknesses created by building collection capability into systems. The third part is prescriptive, with separate sets of recommendations for governments, for corporations and for individuals. Read it as background before writing a privacy policy or specifying an analytics event.
Description via Product Digest.
Topics
Related concepts
- Responsible AI in practiceBias, fairness, robustness, safety and veracity treated as things that can be measured and acted on, plus the legal exposure that arrives with generative output.
- Securing an AI systemAccess control, encryption and the shared responsibility model applied to machine learning, plus the attack surfaces that only exist because there is a model in the path.
- The EU AI ActFour risk tiers, what each obliges, where the timeline stands after the 2026 amendments, and how a team works out which tier a feature falls into before somebody else does.
- Secure AI and the Secure AI FrameworkSecurity across the machine learning lifecycle, what Google's Secure AI Framework is for and what it became, and the Google Cloud tools the Gen AI Leader exam expects a candidate to name.
- Adversarial machine learningAttacks that target the model itself, rather than the software around it, and why several of them have no clean fix.
- Memorisation and training data extractionWhat a model retains from its training data, how that can be pulled back out, and what differential privacy does and costs.
- Hidden context exposureEverything an application quietly holds in the context window, why a system prompt was never a security boundary, and why leaked context is usually the step before the attack.
- Who governs AIThe layers that actually govern an AI system, from law written long before it existed to the frameworks a company writes for itself.
- Content provenance and watermarkingHow generated content is marked, why detecting it after the fact does not work, and what the law now requires.
- AI governance and complianceThe standards an AI system gets audited against, what data governance means once a model is involved, the review routine that keeps a deployed system accountable and the tooling that does each job.
- Compliance in projectsEstablishing which legal, regulatory, contractual, industry and internal rules bind a project, designing for them early and holding the evidence that proves this was done.