From copilots to chatbots, artificial intelligence is everywhere in modern workflows. But as its influence grows, so do the AI security risks. Today’s AI systems aren’t just tools—they’re targets. And unlike traditional software, they don’t need to be breached to be compromised. They just need the wrong input at the wrong time.
In this guide, we’ll explain what AI security actually means—and how AI is opening up new ways for attackers to exploit systems and data.
AI Security and AI Security Risks Defined
AI security is the practice of protecting AI systems from threats that can distort their behavior and expose sensitive data. Unlike traditional software, which runs on fixed code, AI models evolve based on the data they consume and the feedback they receive over time. This makes them more dynamic—but also more vulnerable.
Things like poisoned training data, manipulated input, or unauthorized access to the model’s internal logic can quietly alter how the system performs. From the outside, it may appear to function normally. But in reality, it could be generating flawed, biased, or insecure results that are difficult to detect and fix.
These kinds of threats fall under the umbrella of AI security risks—vulnerabilities that can emerge at any point in the AI lifecycle, from development and training to deployment and real-world use.
What makes these attacks especially dangerous is how they exploit the nature of AI itself. Rather than causing visible crashes or downtime, attackers target how models learn and behave. And that makes their manipulations harder to spot and stop than conventional cyber threats.
That’s why artificial intelligence security is now central to the broader cybersecurity conversation. Securing the entire stack—data, models, infrastructure, and interactions—is key to keeping AI systems reliable, safe, and aligned with their intended purpose.
Top 9 AI Security Risks
AI systems introduce new ways for things to go wrong. Unlike traditional software, they do more than just run code—they also learn from it. This opens the door to attacks that target how models are trained, how they behave, and what they reveal.
Here are some of the most pressing artificial intelligence security risks to be aware of.
1. Adversarial Attacks
AI models interpret data differently than humans do—a feature bad actors can take advantage of. By feeding a model carefully crafted inputs that appear normal to the human eye, they can force it to make incorrect decisions. These adversarial attacks target how the model “thinks,” potentially causing a threat detection system to overlook malware or a facial recognition system to misidentify someone. The danger lies in how easy it is to erode AI’s integrity at scale.
2. Data Poisoning
Every AI model is only as good as the data it learns from. In a data poisoning attack, malicious actors inject misleading or harmful inputs into the training set. This compromises the machine learning (ML) process, causing the model to ignore certain threats or make incorrect predictions.
3. Model Inversion and Privacy Leakage
Some AI models trained on sensitive information, like personal health records or financial details, may unintentionally leak it through their outputs. In a model inversion attack, adversaries systematically query the model to reconstruct underlying data, often to access private or regulated information. Privacy leakage is one of the most pressing AI security concerns—especially in regulated industries like healthcare and finance, where even partial exposure can carry serious legal and reputational consequences.
4. Backdoor Attacks
Backdoor attacks involve planting a hidden trigger during model training. The model performs normally until it encounters that trigger. At that point, it produces manipulated outputs with no obvious signs of compromise. Because these triggers don’t disrupt standard validation, they often go undetected until it’s too late. In AI code generation tools, for example, a backdoored model might quietly insert malicious or biased logic that blends in with legitimate suggestions.
5. Model Theft and Replication
Attackers can reverse-engineer or replicate AI models by feeding them data and analyzing their outputs. This type of model theft exposes valuable intellectual property and lets adversaries recreate models with the same vulnerabilities. In some cases, they may even use the replicated model to probe for weaknesses and launch targeted attacks against the original.
6. AI-Enabled Social Engineering
Generative AI tools, including large language models (LLMs), make it easy to craft convincing phishing emails, fake personas, and deepfake videos. These AI-powered deception tactics are increasingly difficult to catch with traditional filters or user training. Generative AI security risks like these are growing fast, blurring the line between AI-driven output and deliberate manipulation.
7. API Exploits
Most AI systems operate via APIs, making them adaptable. But without proper authentication or monitoring, APIs are sitting ducks. Attackers can flood endpoints with malformed or malicious inputs or bypass safeguards entirely if controls are weak or missing.
8. Transfer Learning Manipulation
To save time and reduce costs, many teams rely on transfer learning—fine-tuning pretrained models rather than building from scratch. But this shortcut can carry hidden risks. If the original model is compromised or poorly trained, those flaws often carry over. Teams may unknowingly deploy systems with inherited biases, backdoors, and security gaps that they didn’t introduce. These risks are especially critical when integrating external AI models into internal pipelines.
9. Hardware-Level Attacks
AI workloads often depend on specialized chips and accelerators, making the hardware layer a target of its own. Attackers can use side-channel techniques, like monitoring power fluctuations or timing patterns, to extract sensitive information. These hardware-level attacks bypass software defenses entirely, threatening the core integrity of AI computations.
AI Security Risks: Mitigation Strategies and Best Practices
Securing AI systems takes a different mindset, as traditional cybersecurity controls weren’t built for models that can be misused and degraded at scale. The following strategies emphasize building security throughout the AI lifecycle to address risks before they cause damage.
Use AI-Driven Security Solutions
To defend against fast-moving threats, you need tools that match their speed. AI-powered detection and response tools scan massive data streams in real time, using pattern recognition to flag anomalies and respond immediately. It’s just one example of how AI enhances modern cybersecurity by embedding speed and intelligence into your existing infrastructure.
Establish a Strong Data Governance Framework
Remember, every AI system is only as strong as the data it learns from—so establish clear policies for collecting, labeling, validating, and storing it. Scrutinize third-party data sources to avoid introducing poisoned or biased inputs. Formalize these processes across the data lifecycle, and use established frameworks like the OWASP Top 10 for LLMs and generative AI to guide your security posture.
Monitor Continuously and Audit Frequently
AI models aren’t static—they can degrade over time or behave differently as input conditions change. That makes continuous monitoring a must. Look for signs of performance drift, tampering, or adversarial inputs. Schedule regular audits, both automated and manual, to identify risks that surface post-deployment.
Align AI Security With Secure-by-Design Principles
Secure-by-design doesn’t stop at code—it also applies to AI development. Build security into every stage of the AI lifecycle, from data sourcing and algorithm selection to testing, deployment, and decommissioning. Maintain early-stage visibility throughout the software development lifecycle (SDLC), and use threat modeling tailored specifically to AI workflows.
Maintain Human Oversight and Response Preparedness
Even the most advanced AI models can make mistakes, especially in edge cases. That’s why human oversight is still necessary. Security teams need to be able to see how AI models make decisions and have the authority to intervene when something seems off. Prepare for AI-specific incidents like model poisoning or adversarial input manipulation with clearly defined response plans. As the relationship between security and artificial intelligence evolves, staying prepared takes a strong mix of advanced tools and informed human judgment.
Benefits of AI in Cybersecurity
AI gives security teams a wider, more immediate view of what’s happening in their environments. When applied thoughtfully, it’s easier for teams to find hidden threats sooner and respond faster.
Enhances Threat Detection
AI excels at finding weak signals in massive datasets. It sifts through traffic, logs, and user activity, hunting for the slightest signs of compromise—things that human analysts might overlook. This allows for earlier detection and a clearer picture of how threats unfold.
Automates Incident Response
Once AI detects a threat, it instantly triggers containment steps. It might isolate an infected device or kick off a remediation workflow. Automating these responses contains damage and accelerates recovery, which is especially critical when incidents escalate in the blink of an eye.
Improves Vulnerability Management
AI prioritizes what to fix first. It finds known vulnerabilities and contextualizes them. But it also assesses factors like exploitability and threat activity to rank them by risk.
Strengthens Behavioral Analytics
By learning what typical user behavior looks like, AI flags outliers like strange login times, unusual file access, or unexpected data transfers. These models help uncover insider threats and account takeovers that might otherwise go unnoticed.
Address AI Security Risks With Legit Security
AI has launched software development into uncharted territory, and most AppSec tools weren’t designed for AI-driven workflows. Legit Security’s AI-native ASPM platform is. It maps where AI lives across your SDLC and enforces security from code to cloud.
Legit detects risks like hardcoded secrets, policy violations, and model misuse in real time, without slowing development. You get the visibility to focus on what matters, the context to act quickly, and the coverage to secure modern, AI-powered software from day one.
Book a demo to see how Legit Security keeps you ahead of AI security threats.