All news with #training pipeline security tag

AI Risks Push Integrity Protection to Forefront for CISOs

🔒 CISOs must now prioritize integrity protection as AI introduces new attack surfaces such as data poisoning, prompt injection and adversarial manipulation. Shadow AI — unsanctioned use of models and services — increases risks of data leakage and insecure integrations. Defenses should combine Security by Design, governance, transparency and compliance (e.g., GDPR, EU AI Act) to detect poisoned data and prevent model drift.

Securing AI: End-to-End Protection with Prisma AIRS

🔒Prisma AIRS offers unified, AI-native security across the full AI lifecycle, from model development and training to deployment and runtime monitoring. The platform focuses on five core capabilities—model scanning, posture management, AI red teaming, runtime security and agent protection—to detect and mitigate threats such as prompt injection, data poisoning and tool misuse. By consolidating workflows and sharing intelligence across Prisma, it aims to simplify operations, accelerate remediation and reduce total cost of ownership so organizations can deploy bravely.

Cloudflare data: AI bot crawling surges, referrals fall

🤖 Cloudflare's mid‑2025 dataset shows AI training crawlers now account for nearly 80% of AI bot activity, driving a surge in crawling while sending far fewer human referrals. Google referrals to news sites fell sharply in March–April 2025 as AI Overviews and Gemini upgrades reduced click-throughs. OpenAI’s GPTBot and Anthropic’s ClaudeBot increased crawling share while ByteDance’s Bytespider declined. The resulting crawl-to-refer imbalance — tens of thousands of crawls per human click for some platforms — threatens publisher revenue.

Data Integrity Must Be Core for AI Agents in Web 3.0

🔐 In this essay Bruce Schneier (with Davi Ottenheimer) argues that data integrity must be the foundational trust mechanism for autonomous AI agents operating in Web 3.0. He frames integrity as distinct from availability and confidentiality, and breaks it into input, processing, storage, and contextual dimensions. The piece describes decentralized protocols and cryptographic verification as ways to restore stewardship to data creators and offers practical controls such as signatures, DIDs, formal verification, compartmentalization, continuous monitoring, and independent certification to make AI behavior verifiable and accountable.

Preventing ML Data Leakage Through Strategic Splitting

🔐 CrowdStrike explains how inadvertent 'leakage' — when dependent or correlated observations are included in training — can inflate machine learning performance and undermine threat detection. The article shows that blocked or grouped data splits and blocked cross-validation produce more realistic performance estimates than random splits. It also highlights trade-offs, such as reduced predictor-space coverage and potential underfitting, and recommends careful partitioning and continuous evaluation to improve cybersecurity ML outcomes.