“Frontier AI” is not just a buzzword. It denotes advanced, general-purpose foundation models with rapidly scaling capabilities and open-ended task coverage. Governments now treat these models as a special regulatory and safety focus area, with implications for procurement, risk management, and compliance in the EU, UK, US - and increasingly across the GCC. Step-by-step: pinning down the definition Start from the government baseline. The UK’s discussion paper describes frontie

Executive take:  Public institutes have begun publishing pre-deployment evaluations  and risk management frameworks . Emulating these processes will strengthen enterprise governance, procurement, and assurance, whether you operate in the EU, UK, US, or GCC. Step-by-step: the evaluation blueprint Start with NIST AI RMF (govern–map–measure–manage). This voluntary standard is becoming the lingua franca for enterprise AI risk programs; it specifies roles, measurement principles,

This article translates those insights into a practical, regulated-bank-friendly AI playbook. By the end, you’ll know what to prioritise, what to sequence, and where AI genuinely moves the needle in your environment - without overpromising or overextending your teams.

The same empirical forces that made frontier models powerful (data/parameter/compute scaling) also push organizations to adopt scaling-aware  safety controls: capability thresholds, staged deployment gates, and stronger red-team and monitoring as models cross those thresholds. Step-by-step: the technical foundations Scaling laws 101. Early work (Kaplan et al.) showed predictable loss improvements from scaling parameters, data, and compute; later, Chinchilla  research emphasiz

The acronym GPT = Generative Pre-trained Transformer. Generative : it produces new text (and, in multimodal variants, other media). Pre-trained : trained on large unlabeled corpora before task-specific use. Transformer : the attention-based neural architecture introduced in 2017. Where “GPT” comes from (milestones) 2018 – GPT-1 : OpenAI shows that generative pre-training  followed by minimal adaptation helps across NLP tasks. 2020 – GPT-3 : Scaling to 175B  parameters yields

Why Size Matters? The short answer LLMs (large language models) : broad, general-purpose capabilities, typically with very high parameter counts and wider context windows—excellent for open-ended reasoning and multi-domain tasks. SLMs (small language models) : fewer parameters, narrower scope, optimized for latency/cost/on-device or domain-specific tasks; often ideal when you need speed, privacy, or constrained hardware. Why “size” affects capability, cost, and latency Capaci

Plain definition RAG (Retrieval-Augmented Generation)  combines a generator  (LLM/SLM) with a retriever  over your trusted knowledge sources; the model conditions on retrieved passages to produce grounded answers. Original formulation: Lewis et al., 2020 (NeurIPS). Why RAG helps? Reduces hallucinations , provides provenance , and enables freshness  by pulling up-to-date documents at answer time. (Motivation from the original RAG paper and vendor architecture guides.) 2025 arc

Government baseline The UK/US AISI  pre-deployment eval of o1  shows public sector expectations: domain-specific tests (cyber, persuasion, biosecurity), red-team procedures, and publishable summaries. Pair this with NIST AI RMF  (govern–map–measure–manage) for lifecycle discipline. Your operating loop Threat model.  List misuse risks by domain (sector + AISI domains). Adversarial testing.  Run jailbreak and tool-use red-teams; include autonomous-agent behavior and data leakag

Scaling drives capability jumps; leading labs now publish thresholded safety policies . Using them as templates will lift your governance to frontier-grade. What does each framework require? Control theme DeepMind FSF (2025) Anthropic RSP / ASL OpenAI Preparedness v2 (Apr 15, 2025) Triggering events Critical Capability Levels (CCLs) incl. deceptive-alignment risk; stronger security by CCL Capability Thresholds → escalate to ASL-3 safeguards Tracked Categories (Bio/Chem, Cyber

Plain definition (policy-aligned). Governments and leading labs use the frontier model  to mean a highly capable, general-purpose foundation model  (typically transformer-based) whose fast-scaling abilities can introduce severe, dual-use risks . The term is prominent in the UK/US AI Safety Institutes’  joint work and in lab safety policies (DeepMind FSF, Anthropic RSP, OpenAI Preparedness). How does this relate to Foundation & GPAI? Foundation model:  trained on broad data, a

Artificial Intelligence (AI) is no longer an experimental playground. In 2025, the debate is no longer whether AI matters, but how quickly organizations can validate its value. According to McKinsey’s State of AI report (2025) , 78% of organizations reported using AI in at least one business function in 2024, and 71% now report regular use of generative AI in at least one function. In Dubai, the DUB.AI blueprint (2024)  seeks to accelerate adoption and contribute AED 100 bil

As AI continues to transform industries, the demand for AI-related skills is skyrocketing. In 2025, businesses will increasingly seek...