The AI boom is driving rapid innovation alongside evolving threats, resulting in both enhanced security measures and increasingly sophisticated AI-powered security threats.
As malicious actors are increasingly exploiting the capabilities of AI, proactive security measures are any business’s greatest defence, making planning for 2026 essential.
Many security leaders may be wondering: if my defences are working now, why worry about next year? In short, the defence mechanisms that have served your organisation in 2025 will simply not be agile enough to deal with 2026’s threats.
From phishing 2.0 to sophisticated risk management and threats powered by generative AI, these dangers are evolving rapidly, leaving traditional security methods in the dust.
Businesses that want to prevent data breaches proactively, model inversion attacks and intelligent phishing strategies should plan for these emerging threats. It’s always better to defend against cyber security risks than deal with a data breach post-attack.
Let’s take a look at key AI security risks in 2026 and how to prepare.
What are AI security risks?
AI security risks expand beyond just technical exploits. They also include human trust, data, and model integrity, all of which pose equally significant risks to your overall security posture.
As a result, many of the traditional penetration testing methods are now outdated, as they lack the agility and continuous assurance needed to defend against these new vectors that attackers exploit.
Large language models (LLMs) and generative AI models broaden the traditional attack surface by introducing not only conventional technical threats but also unique attack vectors such as prompt injection attacks, data poisoning, and model inversion attacks.
It can feel daunting to venture into this new area of security vulnerabilities, particularly with emerging AI threats becoming so frequent.
However, taking a proactive stance to secure your environments is one of the most effective steps you can take as a security leader, and a surefire way to ensure organisational AI safety in the future.
How do AI security risks impact businesses?
AI security risks are a hot topic for businesses in any industry right now. The key for any security leader is to mitigate risks emerging from AI usage, while also empowering their team with the most efficient tooling. However, when improperly implemented, AI integration can pose a range of risks to businesses. Here are just a few of the key consequences of insecure AI usage:
Destabilised investor trust
Poor evidence of AI security can complicate decision-making processes for potential investors, as most will be aware of the rising cyber risks.
If you’re a smaller organisation aiming to receive funding, asserting yourself as attuned to AI systems and their associated risks is essential.
Causes compliance complications
AI security risks can cause compliance complications with many major regulatory frameworks, such as ISO27001, the EU AI Act, and the General Data Protection Regulation (GDPR).
Decreases brand value and could lead to negative PR
Poor AI security could reduce customer trust. No consumer is likely to invest in a business known for cybersecurity risks, and this extends to AI-related threats also.
Top 7 AI security risks to be aware of in 2026
AI-driven social engineering and phishing
AI technologies have transformed the credibility and production speed of deepfakes and phishing emails, created to trick employees or customers into believing they are legitimate communications. From there, attackers exploit employee trust to collect sensitive or proprietary data.
Thanks to AI’s scalability in executing these attacks, incidents of fraud, credential theft, and erosion of trust have surged dramatically.
Mitigation Strategies
Phishing detection training, increased employee awareness through structured training and information, and simulated phishing attacks can all support you in evaluating your organisation’s resilience against AI-powered social engineering.
Social engineering penetration testing can also assess your team’s response to simulated threats, improving your cyber operations by highlighting areas for improvement.
Advanced model manipulation and AI supply chain attacks
AI supply chain attacks pose a range of potential risks for businesses. By exploiting vulnerabilities, hackers compromise data, models, plugins, or dependencies, injecting malicious behaviour to manipulate legitimate model training.
These data poisoning attacks can cause biased outputs, data leaks, and unauthorised system access, all corrupting your business’s AI systems for malicious gain.
To mitigate these risks, be vigilant in validating the data output from your AI systems. Consider maintaining a Software Bill of Materials (SBOM) to keep on top of all the components and dependencies within a software application, making the identification of irregular behaviour a lot easier.
LLM pentesting is also an excellent way to review your AI model for potential vulnerabilities. Using real-world scenarios, attackers exploit common vulnerabilities within your AI, testing for weaknesses. Unique to typical pentesting, LLM testing takes into account threats specific to AI- such as prompt injection attacks– and is a sure-proof way to stay ahead of emerging threats.
Agentic AI / autonomous AI threat surface
Agentic or autonomous AI systems can act on their own, carrying out reconnaissance or launching exploits without waiting for human instructions.
This raises the threat of continuous, highly automated attacks that adapt faster than traditional security measures can respond. Because these systems can scan for weaknesses and strike at machine speed, they may bypass controls designed for human-driven threats.
To minimise the risk of agentic AI vulnerabilities, organisations should limit how much autonomy such AI systems are given, closely monitor their behaviour, and regularly run LLM red-team simulations to identify gaps.
Shadow AI and unmonitored AI use
Although AI can be helpful to employees, when used unsanctioned, it can pose a unique subset of risks. Unsanctioned AI tools (typically used by employees) accessing sensitive data without oversight can cause uncontrolled data exposure and even compliance violations, leaking private business information.
Enforce clear AI policies throughout your organisation to ensure employees are well educated on the potential risks of AI misuse.
AI governance and security gaps
Weak AI policies, standards, or oversight in model deployment can all increase the risk of regulatory penalties and loss of customer trust for businesses.
The best way to mitigate and manage these risks is through the implementation of AI governance frameworks and periodic audits. AI lifecycle security controls are a further effective method of assuring the security of your AI models. Pay attention to relevant AI regulatory frameworks and how they may impact your industry or organisation.
Data privacy, insider threats, and model leakage risks
AI systems can expose sensitive data if training sets are insecure, if insiders misuse access, or if attackers pull information from a model. Leaks like these can reveal personal details, intellectual property, or trade secrets. To reduce the risk, organisations should use privacy-preserving methods such as differential privacy, limit who can access data and models, and monitor internal activity.
The adversarial AI arms race
It’s no secret that there’s an ongoing AI arms race between attackers and defenders. With AI tooling, attackers can automate attacks at scale, deploying phishing, malware, and reconnaissance faster than defenders can respond. This accelerated attack scaling is leading to the industrialisation of cybercrime, making it a more prominent and complex threat than ever before, and leaving traditional defences in the dust.
Deploying AI-based threat detection is an excellent defensive measure against scaled automated attacks. LLM red teaming, or continuous red-teaming, can effectively support in flagging issues within your AI and model development that may be the consequence of automated attacks.
How businesses can safeguard against AI cybersecurity risks
To protect against evolving AI security risks, businesses should adopt proactive defence strategies. Key measures include AI threat modelling and continuous testing to spot vulnerabilities early. Data validation and provenance controls help maintain data integrity, while vendor and plugin risk assessments reduce supply chain threats.
Conducting LLM pentesting and red team exercises simulates real-world attacks to strengthen security. Establishing AI governance and awareness programmes promotes a culture of accountability and informed risk management.
Together, these steps help businesses mitigate AI risks, safeguard sensitive data, and maintain trust in their AI systems as AI innovation continues to advance in 2026 and beyond,
AI Security Readiness Checklist for 2026
Stay one step ahead of evolving threats and gain confidence in your security strategy with OnSecurity’s AI-augmented penetration testing services, designed to empower clients with continuous security insights through our consultative, platform-based approach. Get an instant quote here today.
