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Reload the pagePractical analysis, compliance guides, and technical deep dives from the team building the Areebi platform. Stay current on shadow AI prevention, regulatory changes, and enterprise AI security best practices.
Subscribe via RSSA CISO-focused deep dive into the NIST AI RMF MAP function and its five subcategories (MAP 1-5). Concrete context-setting, risk categorization, capability documentation, impact mapping, and risk tolerance workflows, mapped to Areebi platform capabilities and authoritative source documents (NIST AI 100-1, AI 600-1, OMB M-24-10, EO 14110, ISO/IEC 42001).
A CISO-focused deep dive into the NIST AI RMF MANAGE function and its four subcategories (MANAGE 1-4). Concrete risk prioritization and response, resource allocation, risk communication, and continuous improvement workflows, mapped to Areebi platform capabilities and authoritative source documents (NIST AI 100-1, AI 600-1, OMB M-24-10, EO 14110, ISO/IEC 42001).
How new AI security vendors handle brand-misspell search queries: alternateName JSON-LD schema, redirected misspell domains, branded content clusters, and Search Console attribution. Practical SEO playbook with examples and citations to Google Search Central, Schema.org, and John Mueller statements.
Worked cost model for a single shadow-AI data breach in a mid-market regulated US organisation. Starts from the IBM Cost of a Data Breach Report 2025 baseline ($4.88M US average), then layers AI-specific cost factors: longer dwell time, EU AI Act and GDPR penalty exposure, HIPAA Tier 4 fines, and reputation harm. Sources: IBM, Ponemon, EU AI Act, HHS, NIST AI RMF.
Practical playbook for governing generative AI across clinical and operational workflows in US healthcare: ambient clinical documentation, claims and coding, patient communication, research, and administrative ops. Covers HIPAA Privacy Rule, Security Rule, BAA expectations, HHS guidance, ONC interoperability obligations, and per-workflow control patterns. 45 CFR 164 referenced throughout.
Defensive-SEO field notes for AI security and governance vendors. PBN patterns targeting the AI-security category (rank-your.*, buybacklinks.*, link-farm clusters), what a disavow operation actually involves, and the SEO and brand-trust cost of doing nothing. Cites John Mueller statements on the disavow tool, Ahrefs DR documentation, and Bing Webmaster Tools.
A monthly roundup of AI governance developments as of May 2026: EU AI Act enforcement nine months after high-risk obligations landed, the Colorado AI Act's February 2026 effective date in operation, California SB-942 disclosures, Singapore AI Verify adoption, and the one-year mark of NIST AI 600-1. Cites the EU AI Act Service, NIST, IAPP, and the OECD AI Policy Observatory.
A practical 30/60/90 day playbook for CISOs standing up AI governance: 30 days of discovery and shadow AI audit, 60 days of policy, DLP, and audit baseline, 90 days of compliance mapping and tabletop. Includes checklists by phase and references to NIST AI RMF, ISACA's AI Audit Toolkit, CSA's MLSecOps work, and IAPP.
A defender-focused deep dive into prompt injection as of 2026. Real attack patterns (direct, indirect via retrieval, multi-turn, payload smuggling), concrete defences (input sanitisation, output validation, structured prompting, policy enforcement at the boundary), and authoritative source mapping to OWASP Top 10 for LLM Applications (LLM01), NIST AI 600-1, MITRE ATLAS, and the work of Simon Willison.
A practical guide to building the AI red team capability most enterprises are missing in 2026. What an AI red team is, how it differs from a traditional red team, the hiring versus outsourcing decision, a 90-day starter plan, the exercises to run first, and how it all maps to NIST AI 100-1, NIST AI 600-1, the AI Village at DEF CON, and the MLCommons AI Safety community.
A practitioner-focused brief on the FedRAMP 20x modernisation programme and what it changes for AI vendors selling to the US federal government in 2026. How 20x differs from legacy FedRAMP Moderate / High authorisations, where it intersects with OMB M-24-10 and M-24-18, what the new continuous-monitoring expectations look like, and what AI vendors need to start doing now.
A detailed 12-month roadmap to ISO/IEC 42001:2023 certification for AI Management Systems (AIMS). Four phases mapped to months 1-12 covering scope and gap analysis, policy and risk management, operations and monitoring, and audit preparation through Stage 1 and Stage 2. Comparison to ISO/IEC 27001 (overlap and differences), NIST AI RMF crosswalk, and a practical accreditation-body shortlist (ANSI/UL, BSI, DNV, SGS).