Viewpoint: The AI security frameworks you need for 2026

Data Points, Viewpoints 16th September 2026

By Cameron McAnsh

With the variety of LLMs, Co-Pilots, agents, and AI-powered vibe-coding tools available today, most of us have tried AI in some capacity. Some businesses have built extensive apps and ecosystems utilising AI, but many have overlooked how best to secure their AI-powered apps and processes.

In the rush to deploy new technology, security and governance often take a back seat to the promises of unlocking value and never-before-seen productivity gains. Email, for example, is inherently insecure, but it’s far quicker than the alternative, which is why we’ve spent the last 25+ years securing what was never meant to be secured. Most AI-powered apps deployed in 2025 remain relatively insecure, lacking the robust security controls that might otherwise limit their potential.

Securing AI – time for a new approach?

2026 will reveal the AI security ‘dark side’. Indeed, the lack of governance around AI tools has already become evident in the UK with the possible ban on Elon Musk’s Grok AI image generator. This won’t be the last. It’s only a matter of time before a trusted high-street retailer fumbles its AI deployment, exposing customer data or temporarily shuttering operations until technology teams resolve the breach. Jaguar Land Rover’s 2025 cyberattack, though not AI-related, effectively closed its business for 5 weeks.

Unlike email and traditional compute systems, securing AI presents a slightly different challenge. Natural language prompts generate non-deterministic code, creating new challenges for IT professionals and business leaders. Even if a business believes it has secured its AI implementation, how does it know the effort succeeded or proved appropriate?

Five Essential Frameworks

AI is a rapidly evolving technology. What we knew and understood in January 2025 is now outdated compared to what we know today. Despite such rapid evolution, the best approach for securing AI-powered systems and apps remains grounded in the proven strategies we’ve learnt in securing our ‘old’ technology, over the past 30+ years.

Throughout 2025, we’ve seen a number of frameworks emerge that can guide businesses to make sound decisions regarding AI security. Depending on your use case, one or more frameworks may prove essential for demonstrating competence in AI usage and solutions to customers, partners, or investors. New frameworks will emerge specific to industries, but here are five that offer an excellent starting point.

ISO 42001 (AI Governance Framework)

ISO 42001 is the first international standard for AI governance, providing a structured framework for ethical, legal, and operational oversight of AI systems. It aligns with broader ISO management systems (e.g., ISO 27001 for cybersecurity) and emphasises transparency, accountability, and risk management. The standard covers areas like AI ethics, bias mitigation, and compliance with regulations (e.g., GDPR, EU AI Act). Organisations adopting ISO 42001 demonstrate competence in AI governance, which is critical for customer trust, regulatory compliance, and investor confidence. Its modular approach allows customisation for industries like healthcare (AI diagnostics), finance (AI-driven lending), or defense (autonomous systems).

AIVSS OWASP

AIVSS (AI Vulnerability Scoring System) by OWASP is a structured approach to assessing AI security risks by categorising vulnerabilities into Attack Vector, Impact, and Vulnerability Severity. Inspired by traditional security frameworks like CVSS, AIVSS provides a quantifiable risk matrix for AI models, helping organisations prioritise fixes (e.g., adversarial robustness, data poisoning, or model inversion attacks). Compatible with OWASP’s broader security standards, AIVSS is particularly useful for compliance-driven industries (e.g., healthcare, finance) where AI audits are mandatory. Its simplicity makes it accessible for teams new to AI security, while its granularity suits advanced threat modeling.

The National Institute of Standards and Technology (NIST) developed the AI RMF as a comprehensive, risk-based framework for managing AI systems across lifecycle stages from development to deployment. It aligns with broader NIST cybersecurity guidelines, emphasising risk assessment, mitigation, and monitoring. The framework divides AI security into functions: Govern, Map, Measure and Manage. Its adaptability makes it a cornerstone for enterprises seeking certifiable AI security.

NIST AI Risk Management Framework (AI RMF)

MAESTRO (Multi-Agent Environment, Security, Threat, Risk, and Outcome)

MAESTRO is a framework designed for multi-agent systems, addressing security, risk, and threat management in collaborative AI environments. It emphasises deterministic outcomes by structuring agent interactions through formalised contracts and governance models. Ideal for enterprises deploying AI-driven workflows with interdependent agents, MAESTRO ensures accountability by defining clear roles, responsibilities, and failure modes critical for systems like autonomous decision-making platforms or supply chain AI agents. Its modular approach allows adaptation to evolving threats, making it a forward-thinking tool for DevSecOps teams managing complex, decentralised AI ecosystems.

Cloud Security Alliance’s AICM (AI Control Matrix)

The Cloud Security Alliance’s (CSA) AICM evaluates the maturity of AI security controls in cloud environments, scaling from Basic (emerging practices) to Advanced (proactive governance). Designed for enterprises leveraging AI in hybrid or multi-cloud settings, AICM assesses factors like data privacy, model explainability, and adversarial defenses. It’s particularly relevant for companies using AI for fraud detection, customer analytics, or predictive maintenance, where cloud-native security is paramount. CSA’s model bridges gaps between AI security and traditional cloud compliance (e.g., ISO 27018), offering a roadmap for continuous improvement in AI-driven cloud operations.

Which Framework For Me?

Unfortunately, no single framework exists that will solve all your AI security problems. Every industry will use AI in a different way, and every business within each industry will have vastly different use cases. If we look specifically at businesses within the Market Research sector, the technology maturity of any two businesses (that is, how they have embraced and embedded technology within their teams) has a significant bearing on their assumed risks. Using an LLM to redraft poorly worded questionnaires is a different problem than using autonomous AI agents to collect, clean, and process respondent data. It is possible that a small Market Research agency will have a far more complex AI governance challenge than a much larger competitor.

Time For A Strategy

These frameworks illustrate a broader point: AI security is no longer a purely technical concern. It is a governance, trust, and business resilience issue. We’ve all tried the new tools, and been impressed by the new capabilities. Organisations that treat AI security as a foundational capability, rather than an afterthought, will be better positioned to scale safely, comply with regulation, and earn long-term confidence in an increasingly AI-driven economy, whatever challenges emerge in 2026.

Cameron McAnsh is a technology professional with more than 20 years experience in building new and transforming legacy technology platforms and tools. He may be contacted at cameron@technologysolutionsnetwork.io