AI resilience & security are key success factors for companies, industry and public institutions as AI is increasingly being integrated into core processes, creating new requirements for AI security, governance and monitoring. Resilient AI architectures, high data quality, clear governance structures and strong security mechanisms are crucial to ensure the stability, transparency and reliability of AI-supported processes. Organisations that combine these elements holistically create a robust and accountable AI landscape that enables innovation without increasing operational risk

AI as a value driver

Artificial intelligence is driving efficiency, automation and data-based decisions in production, quality control, supply chain and other core industrial areas. However, with increasing use, the dependency on stable and secure AI systems is growing. Failures, wrong decisions or manipulation can have a direct impact on production, supply chains and reputation.

AI Resilience – Stability despite disruptions

As AI‑models dynamically learn to access diverse data sources and interact with production environments in real time, new technical risks arise. Resilient AI‑landscapes therefore require robust architectures, continuous monitoring and clearly defined response mechanisms in order to detect disruptions at an early stage and minimise the impact on critical processes.

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Data protection, data quality and responsible handling of production and operating data

Industrial AI processes sensitive operational data from sensors, machine control systems and logistics, including personal data in some cases. The protection of confidentiality, the transparency of data flows and high data quality are crucial, as incorrect or unsecured data can directly influence the reliability and security of AI models.

Holistic approach for companies, industry and public institutions

Companies that combine AI resilience & security with AI governance create an integrated model of technology, organisation and compliance. The result is a responsible, robust and future-proof AI landscape that enables efficiency and innovation without jeopardising corporate stability.

AI governance as a framework for controllability, transparency and anchoring in the company

AI requires clear governance structures for the transition from pilot projects to productive use. These include defined roles and responsibilities, transparent processes, structured risk and approval procedures and traceable model versioning.

A structured governance framework makes AI systems auditable, controllable and compliant and creates the basis for safe and responsible deployment.

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AI monitoring as the foundation of resilience and security

With the increasing use of AI systems – especially LLM-based agents – the need to continuously monitor performance, model quality, security and regulatory requirements is growing. Traditional IT monitoring is not sufficient for this. AI models are non-deterministic, evolve with new data and interact with company systems in complex agent chains.

Specialised AI monitoring enables companies to identify risks at an early stage, avoid misconduct and integrate AI securely into critical business processes. Key challenges include model and data drift, hallucinations, rising operating costs, safety-relevant rule errors and agent decisions that are difficult to understand. With holistic AI observability, companies create a transparent and auditable AI landscape that meets regulatory requirements and provides a stable basis for AI scaling.

Our portfolio of services

KPMG supports companies in developing and operating AI systems in a secure, resilient and compliant manner.

Our service portfolio comprises a holistic offering of cyber security, AI governance and AI operating models that accompanies organisations throughout the entire AI lifecycle - from strategy and architecture to risk and security analyses and the secure operation of AI systems.

Our service portfolio supports organisations in the secure introduction, maintenance and operation of AI solutions.

We support organisations along the entire AI lifecycle - from strategy, architecture and governance to risk and security analyses, use case piloting, go-live support and regular operations.

We also offer a wide range of cyber security services, including:

Identity & Access Management
Cloud and Product Security
Offensive Security
SOC Monitoring
Incident and Breach Response

Our Managed‑Security‑ and Managed‑AI‑Services ensure that critical functions are operated in a permanently performant, compliant and resilient manner. Through the KPMG Trusted‑AI‑Framework, we anchor security, fairness, transparency and data protection as the cornerstones of responsible AI‑utilisation

Our services support organisations in implementing AI‑systems in a secure, transparent and legally compliant manner – in accordance with the EU AI Act, ISO/IEC 42001, GDPR and the Cyber Resilience Act, among others. We offer a structured governance‑framework that holistically combines documentation, risk management, technical evidence, user information and training. Through modular project modules - from use ‑case ‑analyses üto GAP ‑assessments and CE ‑registration – we create clear orientation and efficient implementation. In this way, we enable integrated compliance management that synergistically combines regulatory requirements and secures companies in the long term.

With our AI Risk Lifecycle Framework, we support companies in implementing AI systems securely, reliably and sustainably. Through AI risk‑lifecycle mapping and risk‑to‑control mapping, we identify risks along the entire lifecycle and derive specific security measures. In addition, we offer AI Security Guidance and comprehensive security tests before going live in order to recognise potential vulnerabilities at an early stage. Our solutions can be seamlessly integrated into existing governance and risk management processes and strengthen the cyber resilience of organisations.

Artificial intelligence is increasingly being used by cybercriminals to make fraud schemes faster, more convincing and harder to detect, from cloned voices to synthetic identities, AI enables fraud on an unprecedented scale.With deepfake phishing simulations, for example within MS Teams conferences, we demonstrate realistic attack scenarios and sensitise employees to current threats.

We also carry out vulnerability analyses and develop technical, organisational and procedural measures to prevent AI-supported fraud.

We secure your AI system landscapes holistically against external attacks, system failures and unauthorised access. The focus is on protecting all components of the AI architecture as well as all interfaces along the entire data and model pipeline. We prevent data exfiltration, model manipulation and the compromise of computing resources through robust security architectures, monitoring mechanisms and preventive controls. Special attention is paid to AI agents and multi-agent systems, which require advanced security mechanisms and human-in-the-loop concepts due to their autonomy.

We offer you comprehensive monitoring of your AI systems and AI agents along the entire data, model and decision pipeline. Our focus is on:

Observability of technical key figures
Detection of drift and misbehaviour
Traceability of complex agent processes

Through specialised telemetry, guardrail monitoring and agent tracing, we identify quality issues, safety risks and security risks;problems, safety risks and inefficient decision paths in a timely manner

We secure your AI‑systems where modern technologies open up new risks. Our portfolio includes a comprehensive AI pentesting framework that is based on established security standards and systematically analyses AI systems. Through threat‑led testing, we simulate real attacks such as prompt injection or data leakage to make vulnerabilities visible at an early stage. With full application stack testing, we not only test the model, but your entire infrastructure - from API to cloud.

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FAQ

With the increasing integration of AI into business processes, new risks arise - for example, through manipulated training data, insecure interfaces or uncontrolled modelling decisions. AI security ensures that AI systems can be operated reliably, securely and in compliance with regulations.

AI governance describes organisational and technical structures that companies use to control the use of AI. This includes clear roles, risk analyses, documentation, model monitoring and compliance with regulatory requirements such as the EU AI Act.

AI systems are often not deterministic and change continuously as new data is added. Traditional monitoring approaches often fail to recognise these changes. Special AI monitoring therefore monitors model behaviour, data quality and decision-making logic.

The most important risks include model manipulation, data and model drift, hallucinations of generative AI systems and attacks such as prompt injection or data leakage.

KPMG supports companies along the entire AI lifecycle - from AI governance and risk assessments to security testing and monitoring through to the secure operation of AI systems.