Key Points
AI risks in businesses are the set of legal, operational, economic and reputational threats that an organisation assumes when integrating artificial intelligence systems into critical business processes. They are no longer solely a technological matter: they affect continuity, regulatory compliance and the liability of directors and officers.
In Spain, organisations faced an average of 1,968 cyberattacks per week in 2025, 70% more than in 2023, according to the Security Report 2026 by Check Point Research. This acceleration is driven, among other factors, by automation and the widespread adoption of generative AI. At the same time, Regulation (EU) 2024/1689 (AI Act) has entered into force with phased obligations up to 2027 and penalties that may reach €35 million or 7% of global turnover.
In this article, you will discover the 7 main risk scenarios, who is liable when AI fails, how insurance can transfer part of the financial impact, and what specific steps your organisation should take to prepare.
Why artificial intelligence risks are no longer purely technological
When AI is involved in commercial, operational or contractual decisions, any failure ceases to be an isolated technical incident and becomes a business issue. It affects revenue, reputation, compliance and legal liability at the same time.
“Many organisations still approach AI as a purely technological risk, when in reality it has already become a cross-cutting business risk”, explains Montserrat Recio, senior cyber risk specialist at RibéSalat.
From technological risk to business risk
Traditionally, technological risks were confined to the IT area. With the integration of AI into critical processes — scoring, pricing, customer service, fraud analysis and recruitment — the scope now extends across the entire organisation. A poorly calibrated algorithm can generate commercial losses, client complaints and sanctions proceedings by the AEPD or AESIA within a matter of hours. The risk ceases to be a technical issue and becomes part of the overall business management.
Legal, operational and economic impact of AI
AI-related business risks materialise across three simultaneous dimensions:
- In legal terms, there are breaches of the GDPR, the AI Act or uncertainties regarding the attribution of liability in automated decisions.
- In operational terms, they translate into disruptions to critical systems and production stoppages.
- In economic terms, they range from direct losses and remediation costs to regulatory penalties and damage to brand reputation.
The 7 key artificial intelligence risks in businesses
As AI becomes integrated into business processes, threats emerge that are difficult to identify from day one. The following table summarises the 7 most relevant AI risks in businesses and their main impact.
| Risk name | What it involves (actual mechanism) | Industry example | Main impact | Transfer to insurance |
| Synthetic fraud (deepfake / impersonation) | Use of AI to replicate identity (voice, video, email) in order to induce fraudulent economic or contractual decisions | Legal: impersonation of a partner to authorise a transfer Retail: fraudulent payments to vendors Energy: false orders in critical operations | Direct economic losses + reputational damage | Crime / fraud (social engineering, CEO fraud) + cyber support |
| Critical AI dependency (automation failure) | Automation of key processes with no manual alternatives or operational redundancy | Industry: production stoppage due to predictive system failure Retail: dynamic pricing or logistics outage Energy: failure in control or distribution systems | Business interruption + loss of revenue | Cyber (BI) + continuity programmes + possible extensions in property/operational risk |
| Amplification of biases and errors | Use of incorrect or incomplete data that AI scales and turns into systematic decisions | Legal: faulty legal recommendations Pharma: incorrect clinical or regulatory decisions Retail: failed customer segmentation | Erroneous decisions + claims + regulatory risk | Professional / technological liability + D&O (if it affects governance) |
| Adversarial attacks on models | Manipulation of the model using malicious inputs (prompt injection, data poisoning) to alter its behaviour | Energy: manipulation of control systems Industry: sabotage of automated processes Retail: altered recommendations or prices | Undetected manipulated decisions + operational and financial risk | Cyber + technology liability (if it affects third parties) |
| Data breach (generative AI / shadow AI) | Exposure of sensitive data through the uncontrolled use of external AI tools (prompts, APIs) | Legal: leak of confidential customer information Pharma: leak of clinical or R&D data Retail: exposure of customer data | GDPR penalties + loss of critical information + reputation | Cyber (breach, notification, sanctions) |
| Third-party and AI vendor risk | Dependence on models, APIs, or vendors without control over their security, compliance, or availability | Energy: dependence on critical SaaS vendor Retail: failure of logistics AI vendor Pharma: use of external models in research | Regulatory non-compliance + indirect operational impact | Cyber (third party) + contract review + D&O |
| Automated decisions without human oversight | Elimination or weakening of human controls in critical decisions with a legal or economic impact | Legal: automatic generation of contracts without review Retail: credit decisions or automatic refunds Pharma: automated regulatory decisions | Mass propagation of errors + legal liability | Professional / technological liability + D&O |
None of these risks act in isolation: in practice, a single incident can simultaneously trigger several covers (cyber, liability, D&O and fraud).
Synthetic shadow: the expansion of fraud through fake identities
Synthetic shadow is fraud carried out using AI-generated content that replicates voices, faces and communication patterns with a level of realism that traditional verification systems fail to detect. A CEO can be impersonated via audio in a video call to authorise a transfer; a legitimate client can be mimicked to request contractual changes. The technical barrier has collapsed: current voice cloning models require between 3 and 20 seconds of audio to generate a convincing replica of a real voice (McAfee, The Artificial Imposter, 2023; confirmed in subsequent Consumer Reports tests in 2024), turning any public intervention — an interview, a corporate video, a forwarded voice note — into training material for potential fraud.
According to IBM’s Cost of a Data Breach Report 2025, 35% of cyberattacks using AI are carried out through deepfake impersonation, the second most common vector after AI-generated phishing (37%). In practice, this requires strengthening dual-validation protocols and advanced biometric controls, since corporate email or a phone call are no longer sufficient proof of identity.
Ghost absence: when automation paralyses the business
Ghost absence refers to business interruption caused by the failure or outage of an AI system on which the company has come to depend. A pricing algorithm may set incorrect prices for hours before anyone detects it; an automated customer service system may stop responding to thousands of clients without prior warning.
“The lack of fallback plans turns these failures into direct risks to business continuity”, warns Montserrat Recio.
The solution lies in designing contingency plans with equivalent manual processes for the most critical workflows.
Mirror ecosystem: how AI amplifies internal errors
The mirror ecosystem is the effect whereby AI does not correct the internal errors it receives, but instead reflects them at scale. If AI is trained on imperfect data, it doesn’t just replicate errors; it amplifies them and embeds them into decision-making. In addition, if historical client classification contains biases or outdated information, the model institutionalises them. A credit scoring system may end up discriminating against entire groups; a recruitment AI may systematically reject valid candidates. The result is distorted business decisions, a poorer user experience and, in the worst case, claims for algorithmic discrimination leading to sanctions under the AI Act.
Adversarial storm: attacks that manipulate system intelligence,
Adversarial storm refers to attacks specifically targeting AI models to alter their behaviour. This includes data poisoning (corruption of training data), prompt injection (malicious instructions hidden in texts or documents) and the manipulation of responses through specially designed inputs.
The defensive response window has shrunk dramatically: according to CrowdStrike’s Global Threat Report 2026, the average time between initial intrusion and lateral movement within the network (breakout time) fell to 29 minutes in 2025, with a recorded low of 27 seconds. This acceleration is driven, among other factors, by attackers’ use of agentic AI.
The trend goes even further: the first fully autonomous cyberattack executed entirely by AI, without human intervention, has already been documented, raising a scenario in which developers of advanced models are considering limiting their distribution to prevent misuse against critical infrastructure.
In November 2025, Anthropic disclosed the first documented case of a large-scale cyberattack carried out predominantly by agentic AI. In September 2025, the company detected an espionage operation attributed to the group GTG-1002, allegedly state-sponsored by China, which used Claude Code following a role-play jailbreak (posing as a defensive cybersecurity firm). The AI carried out 80–90% of the tactical work autonomously against around thirty targets — technology companies, financial institutions, chemical companies and government agencies — with human involvement limited to strategic decisions.
Silent breach: invisible yet critical data leaks
A silent breach is the leakage of sensitive information through the everyday use of AI tools, without any external intrusion. An employee pasting client data into ChatGPT to draft an email, a developer uploading proprietary code to a coding assistant, or an API logging prompts containing confidential information: these are all potential leakage channels.
Today, more than 71% of employees use AI tools that have not been approved by their organisation (Reco, 2025 State of Shadow AI Report), a phenomenon that leaves the true exposure of confidential data outside management’s visibility. IBM’s Cost of a Data Breach Report 2025 estimates an additional average cost of USD 670,000 for breaches linked to shadow AI, which are present in 20% of incidents. All of this creates direct exposure to penalties under the GDPR.
Ghost chain: hidden risks in vendors and third parties
Ghost chain refers to the risk arising from dependence on technology vendors, external APIs and third-party models over which the company has no direct control. When an AI vendor suffers an outage, a policy change or a breach, the impact is immediately transferred to the organisation using it. This includes unilateral changes to terms of service, model withdrawals, the vendor’s compliance issues with the AI Act, or security incidents within its infrastructure. The company may find itself in breach of regulations without having changed anything itself.
Blind automation: decisions without human oversight
Blind automation is the vulnerability that arises when human controls are removed from automated decisions with significant impact. Systems that approve credit, set prices, block accounts or handle claims without human validation can propagate an error across thousands of cases before it is detected. Article 14 of the AI Act specifically requires effective human oversight — not merely formal — for systems classified as high risk. Efficiency becomes a vulnerability when there is no review process proportionate to the impact of the decision.
The following diagram summarises the seven risk scenarios addressed in this article:
How these risks impact business continuity
Risks associated with corporate AI don’t usually appear in isolation. They combine and generate chain effects that impact several areas at once: operations, finance, legal and reputation.
“The problem is that they are analysed in isolation when, in reality, they are interconnected and a single incident can have consequences at multiple levels”, notes Montserrat Recio.
Explore this approach in the episode“How to protect your business in an increasingly digital world” of the podcast Historias Aseguradas, where Montserrat Recio analyses the main corporate cyber risks and the role of the broker in building a culture of prevention.
Interconnected risks and chain effects
When an AI system fails or is compromised, the impact rarely remains confined to a single front. A deepfake impersonation can trigger a fraudulent transfer; this can lead to a legal claim from the bank, a data breach report to the AEPD and reputational damage if the case becomes public. One incident, four simultaneous impacts. This cascade effect is what distinguishes AI risks from traditional technological risks.
Real impact on operations and results
AI-related technological and business risks are already materialising in concrete costs. According to the report The State of AI in Enterprises 2026 by Deloitte, 85% of Spanish companies expect to increase their investment in AI in the next financial year, and more than half acknowledge that the gap between strategic ambition and operational capability is generating additional costs in infrastructure, talent and governance, without the real impact on the business yet being fully realised.
This is in addition to legal costs, regulatory notifications, technical remediation and, in many cases, loss of clients. The cost is not theoretical: it is a line in the P&L.
Who is responsible when artificial intelligence fails
One of the major challenges of using AI in business is not only the risk itself, but also the attribution of responsibility when something goes wrong. Automated decision-making, the involvement of multiple providers and the opacity of certain models make it difficult to identify who is accountable before a client, a regulator or a court.
Corporate liability
The organisation using an AI system remains ultimately responsible for the decisions made in its name, even if the technology is provided by a third party. Automated decisions that harm customers, errors with financial impact or improper use of data oblige the organisation to respond both legally and reputationally. The vendor’s role may give rise to recourse actions, but vis-à-vis the affected party, liability always rests with the deploying entity.
New regulatory frameworks (AI Act)
Regulation (EU) 2024/1689 sets out specific obligations that companies must demonstrate, not merely declare. High-risk systems require risk management, data governance, technical documentation, human oversight (Art. 14), and accuracy and cybersecurity measures (Art. 15). Non-compliance may result in fines of up to €35 million or 7% of global turnover for prohibited practices, and up to €15 million or 3% for other obligations. In Spain, the AESIA (Spanish Artificial Intelligence Supervisory Agency) is the competent authority, in coordination with the AEPD.
The AI Act does not operate in isolation. It is complemented by NIS2, which strengthens cybersecurity obligations in essential and important sectors (pending full transposition in Spain), and DORA, a digital operational resilience regulation already directly applicable to financial institutions, insurers and investment firms. For companies, compliance with the National Security Framework (ENS) at high category level is a recognised way to demonstrate alignment with several of these requirements.
Insurance as a key tool against AI risks
Prevention is not enough. Companies also need to anticipate how to manage the financial impact when a risk materialises. This is where insurance plays a central role in the overall risk management strategy.
Which risks can be transferred to insurance
Not all AI risks in businesses are insurable, but many of their financial consequences are. Policies don’t cover AI itself, but the damage its use may cause: fraud through impersonation, business interruption, errors in automated decisions, data breaches, third-party claims, or legal defence costs in regulatory investigations.
Main coverages involved
Effective risk management typically requires a combination of policies that work in concert:
- Cyber: security incidents, data breaches, extortion, system recovery and notification costs.
- Technology liability insurance: errors, omissions or incorrect automated decisions affecting third parties.
- D&O (directors and officers): claims for lack of supervision, poor governance or strategic decisions involving AI.
- Financial crime and fraud: social engineering attacks, CEO fraud, financial deepfakes and fund diversion.
No single cover is sufficient on its own, which makes a combined and coordinated approach essential.
The importance of specialised advice
AI combines technological, legal, operational and economic aspects. Designing an effective risk transfer strategy requires analysing how exclusions are drafted, how different covers interact with each other, and which specific AI scenarios are — or are not — included. The difference is not only having insurance, but how it is integrated into the overall risk management strategy.
How companies should prepare for AI risks in businesses
Managing AI risks in businesses requires going beyond technology adoption and shifting towards a structured governance, control and risk transfer model.
- Assess risk exposure
The first step is to identify where and how AI is used within the organisation, within the broader map of business risks the company faces. This involves answering five questions: what systems are in use, what decisions they automate, what data they use, which providers are involved and what impact a failure would have in each case. It also includes shadow AI: tools employees use without formal approval.
- Review processes and governance
Once exposure is mapped, responsibilities must be defined, human oversight must be established proportionate to impact, data quality must be validated and controls applied to automated processes. Simple measures such as validations, alerts and human escalation thresholds significantly reduce the impact of potential errors and support compliance with the AI literacy requirement under Article 4 of the AI Act, in force since February 2025.
- Adapt the insurance strategy
Many existing policies don’t account for scenarios arising from AI use: automated decision-making, deepfake impersonation, adversarial attacks or AI Act non-compliance. Reviewing the insurance programme with a specialised broker helps identify coverage gaps and align conditions with the new reality.
Artificial intelligence as an opportunity… if risk is managed
AI doesn’t just introduce threats. It also offers a clear opportunity: improve efficiency, optimise processes and enable better decision-making. Organisations that integrate risk management into their AI strategy are better positioned to capture its potential without compromising stability.
The key isn’t to avoid artificial intelligence, but to manage it properly: combining technology, compliance and insurance protection within a holistic business vision.
