AI security risks are the threats and vulnerabilities that target artificial intelligence systems themselves, and they matter because deploying AI turns the model into a new attack surface that traditional security controls were never designed to protect. As organizations embed AI into more of their operations, attackers have gained both a new set of targets, the models and their training data, and a new tool for scaling their attacks. This guide sets out the main AI security risks, from data poisoning to model theft, and the multi-layered approach required to mitigate them.
The distinction that anchors the topic is between using AI to defend a network and securing the AI you deploy. The first is a capability; the second is an obligation, because an AI system that makes or informs consequential decisions is only as trustworthy as its resistance to manipulation. Surveys have tracked a clear rise in AI-enabled attacks in recent years: in a 2024 survey by Deep Instinct and Sapio Research, a large majority of security professionals reported an increase in attacks over the prior year, with most attributing the rise to malicious use of generative AI. The threat is real and growing, which is why treating AI security as a first-class discipline, rather than an afterthought to AI adoption, has become a strategic imperative for security leaders.
Why AI Is a Distinct Attack Surface
AI systems carry vulnerabilities that ordinary software does not, because their behavior is learned from data rather than written in code, which gives an attacker new ways in. A model can be corrupted through the data it learns from, deceived through the inputs it receives, or stolen and repurposed, none of which map cleanly onto traditional application security. The result is a landscape of complex and evolving challenges that threaten the integrity and reliability of the AI systems organizations increasingly depend on.
The clearest evidence that this is a distinct discipline is that the security community has built dedicated taxonomies for it. The OWASP Top 10 for large language model applications, for example, catalogs the most critical vulnerabilities found in LLM-based systems, ranking them by impact, ease of exploitation, and prevalence. For teams building on large language models specifically, that list is the practical starting point, and Elevate’s guide to the OWASP LLM Top 10 security vulnerabilities covers the LLM-specific threats in depth. The broader categories below apply across AI systems generally, whether or not they are language models.
The Main AI Security Risks
The core AI security risks fall into a handful of categories that recur across almost every AI system, and the table sets out the four most consequential with what each is and the damage it does.
| AI security risk | What it is | Primary impact |
|---|---|---|
| Data poisoning | Manipulating training data to corrupt how the model learns | Flawed or skewed decisions, especially damaging in healthcare or finance |
| Adversarial attacks | Crafted inputs that trick the model, including prompt injection | Incorrect or attacker-controlled outputs |
| Model theft | Unauthorized extraction of the model itself | Intellectual property loss and repurposing for further attacks |
| Biased algorithms | Skewed outputs learned from biased data | Discriminatory decisions that can also be exploited |
Each of these deserves a closer look because the mitigations differ. Data poisoning attacks the model at its source, introducing corrupted data so the learning process itself produces a compromised model, which is particularly dangerous in critical sectors where accurate predictions are essential. Adversarial attacks operate at inference time, using subtle input manipulations to make a model misclassify or misbehave; prompt injection is the language-model version, inserting malicious instructions to hijack the model’s output, which NIST has flagged as a severe risk to AI integrity. Model theft extracts the model for reverse-engineering or misuse, turning an organization’s own asset into an attacker’s tool, while biased algorithms create both ethical harm and a security weakness that adversaries can exploit to manipulate outcomes.
How to Mitigate AI Security Risks
Mitigating AI security risks requires a multi-layered approach that combines technology, procedure, and human oversight, because no single control addresses the full range of threats above. The three layers reinforce each other: technical controls harden the model and its data, procedural controls govern the lifecycle around it, and human oversight catches what the automation misses.
Technological Controls
Robust data security is the foundation, since most AI security risks trace back to the data a model learns from or receives. Stringent data governance, including access controls and encryption on training datasets, sharply reduces the likelihood of data poisoning and tampering. Adversarial training, in which a model is deliberately exposed to manipulated inputs during development, hardens it against the attacks it will face in production. Explainable AI techniques preserve the ability to understand why a model reached a decision, which is what lets security teams spot anomalies and flaws, and AI-powered anomaly detection can monitor the system’s own behavior for signs of compromise.
Procedural Controls
Comprehensive risk assessments tailored to AI systems are essential, because a generic security assessment misses the AI-specific failure modes across the model lifecycle from data acquisition through deployment and maintenance. A structured AI risk assessment is the practical instrument for this. Alongside it, an AI-aware incident response plan defines how to identify, contain, and recover from AI-specific incidents, and continuous monitoring gives the real-time visibility needed to catch threats before they escalate rather than after.
Human Oversight
Human oversight is the layer that binds the others together, and it is increasingly a regulatory expectation as well as a best practice. The EU AI Act, for high-risk systems, requires that humans remain in the loop to verify and authorize AI outputs, precisely to avoid the costly errors an unsupervised model can produce. Beyond the individual decision, building a security-conscious culture through regular staff training on AI-specific threats reduces the human error that underlies many breaches. People remain the control of last resort, and designing them into the process is what keeps an AI system accountable.
AI Security Across the Model Lifecycle
One reason AI security risks are hard to manage with a single control is that they enter at different points in the model’s lifecycle, so a useful way to organize defenses is to map each risk to the stage where it originates. At the data acquisition stage, the dominant risk is data poisoning, because that is where corrupted or malicious data can be introduced before the model has learned anything, which makes data governance and dataset integrity the first line of defense. During training, the risk that takes hold is bias, as the model absorbs whatever skew exists in its data, so bias detection and diverse, representative datasets belong at this stage.
Once a model is built, the risks shift from the model’s formation to its exposure. At deployment, model theft becomes a concern, since a model placed into production can be probed, extracted, or reverse-engineered, which calls for access controls and monitoring around the model and its endpoints. In operation, at inference time, adversarial attacks and prompt injection are the live threats, because that is when an attacker interacts with the model directly through its inputs, making input validation, output filtering, and anomaly detection the relevant controls. Human oversight and continuous monitoring span every stage, since they are the safeguards that catch what stage-specific controls miss.
Mapping risks to the lifecycle this way turns an abstract threat list into a concrete plan, because it tells an organization not just what the risks are but where in its own pipeline to put the controls. It also clarifies ownership, since different stages typically belong to different teams, from data engineering through MLOps to the security function. An AI security program that assigns a control and an owner to each stage is far more resilient than one that treats AI security as a single checkpoint before launch.
Where AI Security Risk Management Meets Governance
AI security risk management does not sit apart from AI governance; it is one of its core functions, which is why the mitigations above map directly onto the major governance frameworks. The NIST AI Risk Management Framework organizes exactly this work into a repeatable program, ISO/IEC 42001 provides the management-system structure to run it continuously, and the EU AI Act converts several of the human-oversight and robustness expectations into legal obligations for higher-risk systems. Treating AI security as a governance discipline, rather than a one-time hardening exercise, is what makes it durable as models and threats evolve.
For organizations mapping these obligations to their own AI, the practical bridge is a risk assessment that feeds a governance program, with the regulatory layer sitting on top. Elevate’s EU AI Act compliance checklist shows how the legal obligations sort by risk tier, and the companion view of using AI in defense, rather than securing it, is covered in the guide to AI in cybersecurity. Together they frame the two halves of the same problem: deploying AI effectively and securing the AI you deploy.
There is also a commercial reason to treat this seriously that goes beyond risk avoidance. Enterprise buyers and regulators increasingly scrutinize a vendor’s AI security posture as a condition of doing business, asking how models are protected, how data is governed, and how oversight is maintained. An organization that can answer those questions with a documented program is better positioned to win and keep enterprise customers, which turns AI security from a defensive cost into a commercial enabler. That shift, from AI security as protection to AI security as proof, is why building the program early tends to pay back well beyond the incidents it prevents.
Conclusion
AI security risks are the threats that target AI systems themselves, data poisoning at the training stage, adversarial attacks and prompt injection at inference, model theft, and exploitable bias, and they exist because a learned system fails in ways that coded software does not. Managing them takes a multi-layered response: technical controls that harden the model and its data, procedural controls that govern its lifecycle, and human oversight that stays accountable for consequential outputs.
The organizations that manage these risks well treat AI security as a governance discipline aligned to frameworks such as the NIST AI RMF, ISO 42001, and the EU AI Act, not as a bolt-on to AI adoption. As AI moves deeper into critical operations, that discipline is what separates a trustworthy deployment from a fragile one. To assess the AI security risks in your own systems and build a program to manage them, book a call with an Elevate advisor.
Key Takeaways
AI security risks target AI systems themselves, and managing them takes a layered program rather than a single control.
- AI is a distinct attack surface: because a model’s behavior is learned from data, it can be corrupted, deceived, or stolen in ways traditional application security does not address.
- The main risks are consistent: data poisoning, adversarial attacks including prompt injection, model theft, and biased algorithms recur across AI systems, with LLM-specific variants catalogued in the OWASP LLM Top 10.
- Mitigation is multi-layered: technical controls harden the model and data, procedural controls govern the lifecycle, and human oversight stays accountable for consequential outputs.
- Human oversight is now expected: the EU AI Act requires humans in the loop for high-risk systems, making oversight a regulatory obligation as well as a best practice.
- AI security is AI governance: the mitigations map onto the NIST AI RMF, ISO 42001, and the EU AI Act, so managing AI security risks is best run as a governance program, not a one-time exercise.
FAQs
Q1. What are AI security risks? AI security risks are the threats and vulnerabilities that target artificial intelligence systems themselves, as distinct from using AI to defend against attacks. The main categories are data poisoning, in which training data is manipulated to corrupt the model; adversarial attacks, including prompt injection, which use crafted inputs to make a model misbehave; model theft, the unauthorized extraction of a model for reverse-engineering or misuse; and biased algorithms, which produce skewed outputs that can be both harmful and exploitable. These risks exist because an AI model’s behavior is learned from data rather than written in code, giving attackers ways in that traditional application security does not cover.
Q2. What is data poisoning in AI? Data poisoning is an attack that manipulates the data a model is trained on in order to corrupt how it learns. By introducing corrupted or malicious examples into the training set, an attacker can skew the model’s decision-making so it produces flawed or attacker-favorable outputs. It is especially dangerous in critical sectors such as healthcare and finance, where accurate predictions matter most, because the compromise is baked into the model at its source rather than introduced at the moment of use. Strong data governance, including access controls and encryption on training datasets, is the primary defense.
Q3. What is prompt injection? Prompt injection is a type of adversarial attack specific to language models, in which an attacker inserts malicious instructions into the input to manipulate the model’s output. Because a language model treats much of its input as instruction, a cleverly crafted prompt can override the intended behavior, extract information the model should not reveal, or make it perform actions it should not. NIST has identified prompt injection as a severe risk to the integrity and reliability of AI systems, and it appears prominently in the OWASP Top 10 for large language model applications. Input validation, output filtering, and human oversight of consequential actions are core mitigations.
Q4. How do you mitigate AI security risks? Mitigation takes a multi-layered approach across technology, procedure, and people. Technical controls include robust data governance and encryption, adversarial training to harden models, explainable AI to keep decisions auditable, and anomaly detection to monitor model behavior. Procedural controls include AI-specific risk assessments across the model lifecycle, an AI-aware incident response plan, and continuous monitoring. Human oversight keeps a person accountable for consequential outputs, which the EU AI Act requires for high-risk systems, and staff training builds a security-conscious culture. No single control is sufficient, so the layers reinforce one another as a governance program.
Q5. Does the EU AI Act address AI security? Yes, in part. The EU AI Act requires that high-risk AI systems keep humans in the loop to verify and authorize outputs, which is a direct control against the errors an unsupervised model can produce, and it sets requirements for accuracy, robustness, and cybersecurity that bear on AI security. It is not a security standard in itself, but it converts several security-relevant expectations into legal obligations for higher-risk systems. Managing AI security risks therefore overlaps with EU AI Act compliance, and organizations often address both through the same governance program aligned to frameworks such as the NIST AI RMF and ISO 42001.