AI security risks are the threats that arise specifically from building, deploying, and using artificial intelligence, and they have grown sharply as AI spreads through the enterprise. Traditional security controls were not designed for systems that learn, generate, and increasingly act on their own. This guide covers the top AI security threats organizations face in 2026 and the practical steps to reduce them.
What Makes AI Security Risks Different
Conventional cybersecurity protects networks, endpoints, and data. AI adds a new attack surface on top of that. The model itself becomes a target, the data used to train it can be corrupted, and a system that generates or acts on outputs can be manipulated through nothing more than its inputs. That is why AI introduces threats that existing controls do not fully address.
The Top AI Security Risks
The threats below are the ones security and governance leaders should track most closely.
Prompt Injection
Attackers craft inputs that override an AI system’s instructions, causing it to ignore its guardrails, reveal information, or take unintended actions. It is among the most discussed threats because it requires no special access, only clever text.
Data Poisoning
Adversaries corrupt the data a model learns from, skewing its behavior in ways that are hard to detect after the fact. Poisoned training data can introduce hidden biases or backdoors that surface only under specific conditions.
Sensitive Data Leakage
Employees paste confidential information into public AI tools, and that data leaves the organization’s control. This is one of the most common exposures and is closely tied to shadow AI, the use of unapproved tools.
Model Theft and Extraction
Proprietary models represent significant investment, and attackers attempt to steal them outright or reconstruct them by probing their outputs. The result is lost intellectual property and a copy of the system outside any controls.
Shadow AI and Ungoverned Tools
Unapproved AI tools running outside IT’s visibility are a risk in their own right, because they cannot be secured, monitored, or governed. Ungoverned adoption multiplies every other threat on this list.
Agentic AI and Autonomous Action
AI agents that take actions across systems hold real permissions, so a manipulated or malfunctioning agent can cause damage at machine speed. The more autonomy a system has, the higher the stakes when it goes wrong.
Third-Party and Supply-Chain Risk
Most organizations consume AI through vendors and components they do not control, inheriting whatever weaknesses those suppliers carry. Managing this exposure is a core part of AI vendor governance.
How to Reduce Your Exposure
Reducing these threats is less about a single tool and more about disciplined practice:
- Inventory AI systems and data flows. You cannot protect what you cannot see.
- Govern AI use with policy and approved tools. Sanctioned alternatives curb shadow AI and the leakage it causes.
- Apply least-privilege access. Limit what every system, and especially every agent, is permitted to do.
- Test AI adversarially. Red-team for prompt injection and unexpected behavior before attackers do.
- Protect training data and models. Control access to the data and the models that learn from it.
- Manage third-party AI risk. Hold vendors to the same standard you hold yourself.
- Govern it all within an AI risk program. Tie these controls into a structured, repeatable discipline.
Elevate Consult helps organizations build the governance that keeps these threats under control. The ISO 42001 AI Governance Readiness Bundle is a structured starting point.
AI Security and Governance Frameworks
Recognized frameworks turn scattered defenses into a program. The NIST AI Risk Management Framework names security and resilience as core characteristics of trustworthy AI, and the ISO 42001 standard builds controls for AI risk into a management system. Choosing among them is covered in the guide on AI governance frameworks.
How Elevate Consult Helps Organizations Manage AI Risk
Elevate Consult helps organizations identify, prioritize, and reduce AI security risks within a governance program aligned to ISO 42001 and the NIST AI Risk Management Framework. The work spans AI inventory, policy, access controls, vendor risk, and the testing that keeps these threats from reaching production.
Teams ready to get ahead of these threats can start a conversation with the Elevate team.
Key Takeaways
- AI security risks are threats that arise specifically from building and using AI, beyond what traditional controls were designed to cover.
- The top threats include prompt injection, data poisoning, sensitive data leakage, model theft, shadow AI, agentic AI, and third-party risk.
- AI creates a new attack surface: the model, the training data, and any system that acts on outputs all become targets.
- Reducing exposure depends on inventory, governed tools, least-privilege access, adversarial testing, and vendor risk management.
- Frameworks such as the NIST AI Risk Management Framework and ISO 42001 turn individual controls into a defensible program.
Frequently Asked Questions
What are AI security risks?
AI security risks are threats that arise specifically from building, deploying, and using artificial intelligence, such as prompt injection, data poisoning, sensitive data leakage, model theft, and the misuse of autonomous agents. They extend beyond traditional cybersecurity because the model and its training data become targets in their own right.
What is prompt injection?
Prompt injection is an attack in which crafted inputs override an AI system’s instructions, causing it to ignore its guardrails, reveal information, or take unintended actions. It is one of the most discussed AI threats because it requires no special access, only manipulated text.
How do you reduce AI security risks?
Organizations reduce AI security risks by inventorying their AI systems and data flows, governing AI use with policy and approved tools, applying least-privilege access, testing systems adversarially for issues like prompt injection, protecting training data and models, and managing third-party AI risk within a structured program.
Is shadow AI a security risk?
Yes. Shadow AI, the use of unapproved AI tools outside IT’s visibility, is a security risk because those tools cannot be secured, monitored, or governed. It also multiplies other risks, most commonly the leakage of sensitive data into public AI services.
How are AI security risks different from traditional cybersecurity risks?
Traditional cybersecurity protects networks, endpoints, and data, while AI security risks add a new attack surface: the model can be stolen or manipulated, training data can be poisoned, and systems that generate or act on outputs can be subverted through their inputs. AI requires controls that conventional security tools do not fully provide.