Enterprises are rapidly folding artificial intelligence into their daily operations, using it to power customer service, software development, security analytics, automated decision-making and countless other tasks.
But even as we're thrilled by
what AI can do, we often underestimate
how vulnerable AI systems can be. Large language models, autonomous AI agents, AI APIs, and training pipelines all create new attack surfaces that regular cybersecurity programs have not been designed to protect.
Tenable distinguishes between "
AI for security," or using AI to improve cybersecurity operations, and "
security for AI," which focuses on protecting AI systems themselves from compromise.
The latter is quickly becoming a necessity as attackers undermine AI security through
prompt injection and poisoned training data, as companies deploy insecure APIs and AI models with excessive permissions, and as employees surreptitiously use shadow AI outside of IT oversight.
Fortunately,
exposure management provides a framework to address AI risks by treating AI systems as part of the broader enterprise attack surface. Because AI platforms are not always secure, organizations must use exposure management to continuously identify, validate, prioritize, and remediate AI-related weaknesses before attackers can exploit them.
How AI systems are vulnerable to compromise
AI systems rely on massive datasets, complex workflows, opaque reasoning processes, and extensive interconnectivity with enterprise applications — all factors that traditional security models may fail to address adequately.
Prompt injection attacks are one of the top threats because AI models are often vulnerable to social engineering. Attackers can use natural-language inputs to manipulate AI systems to bypass safeguards, expose sensitive information, or alter their behavior, all without exploiting software vulnerabilities.
Corrupted or manipulated training data can distort AI outputs, weaken safeguards, or embed malicious instructions directly into the AI model itself. Because many organizations rely on third-party datasets, open-source models, and external APIs to train and manage their AI instances, this
data poisoning magnifies supply-chain risks.
Shadow AI increases exposure because well-intentioned employees often upload sensitive data into unauthorized public platforms without understanding the risks. These oft-unseen AI instances can also be compromised by data poisoning or prompt injection, all the more reason why organizations must monitor unofficial as well as authorized AI usage.
Many AI systems are granted excessive privileges or can run with insufficient monitoring. They may end up probing an organization's deepest corners without anyone noticing. And APIs connecting models to enterprise systems may expose sensitive data or provide attackers with lateral movement opportunities if access controls are weak.
How exposure management reveals the AI attack surface
Exposure management helps organizations see where AI-related risk exists because it shows how seemingly small weaknesses can connect across systems, identities, cloud infrastructure, APIs, and AI workflows to create
critical attack chains.
Because many enterprises don't completely understand where AI systems operate or how they access sensitive resources, exposure management begins with visibility. Organizations must catalogue their AI assets, including models, APIs, data pipelines, training environments, autonomous agents, and third-party integrations.
Continuous monitoring of AI assets then helps spot exploitable conditions such as insecure APIs, overprivileged model accounts, exposed datasets, weak authentication controls, or unmonitored external integrations. Tenable recommends that organizations treat AI systems as dynamic attack surfaces rather than isolated applications.
Exposure management also stresses contextual prioritization, as not every AI vulnerability creates an equal amount of risk. Security teams must account for how documented exposures interact with sensitive business data, cloud environments, identity systems, and operational workflows. A vulnerable AI model connected to critical financial systems, for example, presents far greater risk than an isolated internal chatbot.
How to lessen the risks and vulnerabilities of using AI
Effective security for AI requires layered controls that span governance, infrastructure, and identity.
Tenable's guidance offers several best practices organizations can implement to reduce AI-related risk.
First, organizations should enforce least-privilege access so that AI instances and agents can access only the data, APIs, and systems required for their specific functions.
Second, enterprises must secure AI data pipelines. Training datasets, prompts, and inference data all require validation, encryption, monitoring, and governance to prevent poisoning or unauthorized exposure.
Third, organizations should continuously test AI systems for exploitable weaknesses. Exposure management programs should include AI-specific
penetration testing, adversarial prompt testing, API validation, and monitoring for abnormal model behavior.
Governance also plays a role. AI compliance frameworks, auditability, and policy enforcement can ensure that AI systems align with security, privacy, and regulatory requirements.
Finally, organizations must maintain ongoing visibility into shadow AI usage. Because blocking unauthorized AI entirely is rarely practical, enterprises should monitor AI adoption, establish approved usage policies, and implement controls capable of detecting risky interactions in real time.
Implementing security for AI requires a shift in mindset. We cannot assume that an AI system is trustworthy simply because it functions correctly. Instead, we must continuously prove AI resilience through
governance, testing, identity controls, and exposure-management strategies designed for the realities of autonomous, data-driven systems.
