Top 8 risks and challenges of AI adoption

• Your AI attack surface is invisible to legacy security tools. Those security tools can’t find or map rapid, decentralized AI adoption, which creates a layer of shadow AI and exposed services.
• AI risk is a chain. AI exposure is rarely a single asset. It emerges from complex, hidden connections between cloud infrastructure, overprivileged non-human identities, and sensitive data flows.
• Protecting your vast attack surface, including AI, means moving beyond isolated point tools to a unified exposure management platform that can continuously discover AI use, protect AI workloads, and enforce AI policy guardrails.

The rapid, decentralized adoption of AI tools creates an AI exposure management gap across your attack surface. This security gap is largely invisible. It’s a place where your security teams often lack visibility, so they struggle to manage shadow AI use, data flows, and AI infrastructure.

As a result, your organization faces three critical risks from these distributed AI workflows:

1. An invisible attack surface 

   You don't know everywhere your organization uses AI. It lives outside your centrally managed systems — browser extensions, forgotten test deployments, exposed services — quietly expanding your attack surface as shadow AI.

2. Hidden attack paths

   AI workloads create complex risk chains across your infrastructure, identities, and applications. These interconnected parts form high-impact attack paths that isolated cybersecurity tools just can't see or connect.

3. Data leaks 

   Every AI interaction can expose sensitive, protected, or proprietary data. Without visibility and guardrails, your AI workflows, like prompts, uploads, and responses, can accidentally expose sensitive data, intellectual property, and internal knowledge.

See how Tenable One for AI Exposure can help you identify your exposure gap and close it fast.

To close your AI cybersecurity gap, you must find and mitigate the specific vectors that attackers exploit to breach your environment. These AI risks and challenges extend into your underlying infrastructure, the identities that access them, and the data they consume. Together, they unite to create a complex threat landscape that demands a unified exposure management strategy.

Here are some of the top AI risks that contribute to your AI exposure management gap:

1. AI model bias and training flaws

AI risk applies to the AI models you build and the public AI tools your workforce uses. Whether it is an internal AI model you trained on incomplete data or a public large language model (LLM) that hallucinates, reliance on flawed AI can lead to insecure code generation, biased automated decisions, or factually incorrect decisions. You need visibility into these risks to ensure AI-generated outputs don't introduce new liabilities into your environment.

Exposure management to reduce AI risk and challenges: An exposure management platform, like Tenable One, creates a unified inventory of your AI software and libraries, so you can find vulnerable or misconfigured components and prioritize remediation based on real-world risk context.

2. Lack of visibility into decision-making

When AI agents and models make decisions without showing you how or why, your security teams can’t see how they interact with sensitive data or why they request specific permissions. Here, context is imperative so you can understand these hidden connections and trust the AI model output, without fear it exposes protected or sensitive information.

Exposure management to reduce AI risk and challenges: By correlating AI workloads, identities, and data in a single view along with the rest of your attack surface, exposure management gives your security teams threat and business context to find and close hidden attack paths and understand how AI models interact with your sensitive resources.

3. Use of unapproved AI tools

Data leaks can start with well-meaning employees who use unauthorized AI apps to speed up productivity. Without oversight, staff can inadvertently upload documents or paste proprietary code into external models and unknowingly hand your data to an unauthorized third party. You must enforce an AI acceptable use policy (AI AUP) to lock down these data flows and govern exactly which tools are safe to use.

Exposure management to reduce AI risk and challenges: An exposure assessment platform (EAP) can apply policy-based guardrails to guide employees toward secure environments and use data loss prevention (DLP) for AI to find and reduce the risk of sensitive data or intellectual property sharing in AI prompts and uploads.

4. Prompt injection and adversarial attacks

Bad actors can manipulate generative AI. Attackers are already crafting prompts that trick models into generating harmful output or revealing internal logic. You need input validation, monitoring, and safeguards at every layer.

Exposure management to reduce AI risk and challenges: An exposure management solution uses adversarial AI defense capabilities to find prompt injection attempts, jailbreak behaviors, and malicious instructions. It can then alert your security team so they know about active attempts to manipulate your AI systems.

5. Overreliance on automation

If your workforce automates critical decisions, from code generation to customer interactions, without human oversight, you create a direct path for vulnerabilities and operational failures to enter your environment. The best defense is a human-in-the-loop strategy where AI accelerates work, but humans validate the output before it affects your business.

Exposure management to reduce AI risk and challenges: An exposure management tool gives you continuous visibility into AI use and workforce behavior, so your teams can govern AI adoption and ensure that automated workflows and agent interactions align with your security policies.

6. Weaponization of AI

Phishing kits, malware, and deepfakes are all getting an AI boost that makes threat actors even faster and more dangerous. Because these attacks often bypass traditional cybersecurity defenses, your threat models should account for this shift. Expect faster attack cycles, more believable lures, and threats that evolve too fast for signature-based defense.

Exposure management to reduce AI risk and challenges: Exposure management continuously maps your external attack surface to find publicly exposed AI services, APIs, and chat endpoints, so you can close visibility gaps that attackers exploit to launch sophisticated AI-driven campaigns.

7. Shadow and untrusted AI models

The rise of shadow AI, including unapproved browser extensions, SaaS apps, and sovereign models like DeepSeek, introduces software that can bypass your security controls. Employees may unknowingly use low-cost or foreign-hosted AI models that do not align with your data and privacy standards and expand your attack surface beyond your visibility.

Exposure management to reduce AI risk and challenges: Exposure management includes continuous shadow AI discovery to find unapproved AI apps, services, and browser plugins running on your endpoints, so your security teams can manage unsanctioned use.

8. Insecure AI infrastructure and identities

AI workloads often rely on overprivileged non-human identities and complex cloud infrastructure. Misconfigurations here create hidden attack paths that grant attackers access to your most critical data, regardless of how secure the model is.

Exposure management to reduce AI risk and challenges: With integrated AI security posture management (AI-SPM), you can detect misconfigured cloud resources and overprivileged non-human identities to sever the attack paths that expose your AI workloads to compromise.

Want to explore these AI challenges and risks more deeply? See how AI is reshaping the cybersecurity threat landscape and its implications for your security program.