The office may look the same, but the way people work has changed. Machines write emails, flag risks, forecast demand, and make decisions. By 2026, artificial intelligence will be at the heart of every business, from recruiting and serving customers to enhancing cybersecurity, managing finances, and strategizing for corporations.
The shift brings gains in speed and perception, but also a greater attack surface. AI systems generate new failure modes that standard software security programs were never designed to address. The aim is not to stifle innovation, but to help leaders understand where the risk calculus shifts, so they respond proactively rather than reactively.
Why AI security carries financial consequences
Unlike IT-focused security issues, models that determine pricing, eligibility, fraud detection, or regulatory compliance can have direct and indirect impacts on revenue, affecting trust and liability. The business cost of cybercrime continues to rise despite government reports. In 2024, IBM found that the average data breach price was $4.4 million, the highest it has ever been.
Additionally, companies that extensively utilize AI in their operations begin to incur AI-related costs. Spending is highest when an automated system simultaneously broadcasts both an error and too much private information. Companies that introduced AI without establishing safety measures learned the hard way that success does not imply security. Lack of cybersecurity has gone from being a technical issue to a business risk with direct financial implications. There are seven main threats business owners must consider in 2026.
1. Data poisoning
AI models learn from historical and recent data rather than rigid rules, making them susceptible to the quality and integrity of the information on which they were trained. When fed false, biased, or manipulated data, they can easily pick up incorrect patterns as facts.
Poisoned models still perform in some areas. Detection may not surface until after real harm has occurred, such as compromising security monitoring, fraud detection, or an access control system. The longer the poisoning lasts, the more difficult it becomes to correct.
Treat training data as a protected asset. Validate inputs with anomaly detection by documenting data sources and changes throughout the model’s life cycle. Test models over known reference datasets to detect drift before errors occur in high-impact predictions.
2. Adversarial attacks
Adversarial attacks exploit model responses rather than training data. Small input changes can confuse AI, leading to incorrect outputs. These changes are usually invisible to humans but still effective against the model, making them challenging to detect. Adversarial examples have been studied in domains including images, text, and speech.
AI-generated attacks can be dangerous in image recognition, transaction monitoring, and text processing workflows. Well-trained models can also be fooled by inputs that exploit their blind spots. Over time, these methods may become ineffective, as attackers learn to circumvent them. The biggest risk is hackers gaining access to confidential files by learning to exploit an AI model.
Use training to help the model learn what adversarial examples look like. Validation layers can filter out the adversarial examples before they reach it. Monitoring of output patterns can also help identify such behavior.
3. AI phishing
Generative AI has also made social engineering attacks easier, as phishing emails, calls, and impersonations can be better tailored to the context and prompt. Additionally, these messages often have less obvious gimmicks than those used to detect fraudulent activity in the past, making them more challenging to track.
AI-assisted attacks are not limited to initial model training. For example, if the target system uses credential reuse and/or weak authentication, a successful message can lead the attacker to a targeted system. The risk also extends to messaging platforms and voice communications.
To mitigate phishing risk, update training and focus on behavioral changes. Verification that people are who they claim to be should always be in place, especially for sensitive requests involving financial or access actions. Train users to check AI detection tools and require multifactor authentication.
4. Prompt injection
Because large language models (LLMs) require input, prompt injection is a significant risk when using them. Issues occur when an adversarially constructed user command or input attempts to bypass constraints imposed by proper structure. This may result in the release of unintended information, the generation of wrong outputs, or the execution of harmful actions in other systems.
Since prompts are not always distinguishable from other text, malicious ones may not be evident until they are executed. Output from these commands may appear to be a system error, complicating root cause analysis.
Companies can reduce the risk of prompt injection by validating user input. Enforce privilege controls to restrict models’ access to only the systems and data necessary for their tasks. Treat prompts as a potential attack surface in application design and testing.
5. Model copying
AI models require resources and expertise to create, making them valuable assets. Attackers may either steal the model or attempt to reverse-engineer it. Repeated queries enable an opponent to learn proprietary behavior.
Since model theft does not necessarily involve data exports, organizations may not realize that theirs has been stolen. Models available to the public are at the highest risk if left unmonitored. The lost revenue can also reduce the organization’s future competitive viability.
Reduce the risk of copying by locking down models with encryption and access controls. Apply rate limiting and anomaly detection to help identify and mitigate extraction attempts on exposed models.
6. Third-party access
Few companies develop their own AI systems entirely in-house. Most use third-party tools, programming interfaces, or embedded models. Each integration introduces risk to the AI supply chain. A vulnerability could exist in a vendor’s environment and escalate to other systems within the customer’s environment.
As AI adoption proliferates, organizations will find it increasingly difficult to detect and respond to vendor exposures. Few individuals or groups are accountable for incidents. Out of the 13 percent of organizations reporting breaches, 97 percent admitted to lacking proper AI access controls.
To mitigate third-party risk, consider security posture, data handling, compliance history, transparency in incident response, and technical capabilities during vendor evaluation. Clearly state data ownership and responsibility in all contractual documents to prevent ambiguity in the event of a security incident.
7. Compliance issues
The regulation of AI systems is increasing, with governments introducing requirements for data protection, transparency, and algorithmic fairness. If organizations fail to comply, authorities may impose fines or restrict their operations. Leaders should expect enforcement in 2026.
Due to the opacity of AI decision-making, businesses cannot justify disputes during audits and investigations without the proper documentation. Reactive compliance attempts are often costly.
Reduce the risk of noncompliance with internal rules, including oversight committees, bias assessments, and records of model training and deployment. Establish procedures early and maintain accurate records to minimize regulatory friction.
Preventing AI from becoming a permanent liability
The real risk from AI does not come from a particular path organizations follow, but from scaling without clear ownership, oversight, and controls. The one thing these risks have in common is their potential for multiplication over time. Business leaders can audit how they have used AI so far, govern more tightly, and hold people accountable before deciding how to scale. 2026 is the year to adopt oversight while adaptation remains a viable option.
Zac Amos is the Features Editor at ReHack Magazine, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on X (Twitter) or LinkedIn.
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