As GenAI continues its rapid enterprise adoption, most security leaders are no longer asking if AI will be deployed, but rather, how securely. While much of the early attention has focused on training data, privacy, and governance, the greatest risks now lie in inference. Inference is where AI touches live data, customer interfaces, and internal workflows. Inference also happens to be where adversaries are increasingly targeting. Enterprises need layered protections, deeper scanning, and proactive exposure management, especially as GenAI inference systems grow more complex.
Here are five critical risks security leaders must address now.
1. Model Selection Without Security Signals
Choosing the wrong model doesn’t just impact performance—it opens the door to serious vulnerabilities.
Most organizations evaluate models for accuracy, cost, and latency—but security is often left out. Some models contain intentional or accidental backdoors. Others leak sensitive training data. Many lack robust safeguards against malicious prompts or behavior manipulation.
What’s at stake:
- Exposure of confidential training data (via unintended memorization)
- Use of models with undocumented capabilities (e.g., generating malicious code)
- Dependence on black-box models with unknown or outdated defenses
2. Prompt Injection and Its Sneaky Cousin
Prompt injection remains one of the most pervasive and evolving threats in GenAI usage.
Attackers can craft input prompts designed to override safety instructions, exfiltrate sensitive data, or force the model to behave unpredictably. In indirect prompt injection, the attack is buried inside seemingly benign user-generated content that gets incorporated into prompts later (think of a poisoned support ticket).
Why this matters now:
- These attacks are highly dynamic and context-sensitive
- Many models still fail even basic injection resilience tests
- Traditional filters don’t stop novel or obfuscated injections
3. Model Extraction and Reverse Engineering
In production environments, your deployed model becomes a target.
Adversaries can systematically query your AI system to recreate its behavior—effectively stealing your intellectual property. Known as model extraction, this tactic is especially dangerous for enterprises that fine-tune or embed GenAI models into customer-facing products.
Why this matters:
- Competitive IP (e.g., decision logic, language patterns) can be replicated
- Attackers can use cloned models to find weaknesses or create deepfakes
- APIs can be abused at scale unless strong rate limiting and anomaly detection are in place
4. Data Leakage Through Normal Use
Not all data breaches happen through system compromise. In GenAI, the model itself can become a leak vector.
If a model was trained or fine-tuned on confidential or regulated data, it may reproduce that data in output—especially under the right prompt conditions. This is particularly concerning with customer data, healthcare information, financial records, or IP.
Data leakage can come from:
- Model responses that echo internal documents or records
- Misconfigured prompt templates that surface sensitive variables
- Lack of visibility into what’s leaving the model in real-time
5. Business Risk: Brand Damage, Compliance, and Chaos
Beyond technical exploits, inference security failures can damage reputations and violate regulations.
Imagine a chatbot generating offensive content, or a model hallucinating financial advice. These aren’t edge cases anymore, they’re real-world incidents. And as GenAI becomes more deeply embedded in workflows, the operational risk grows.
What’s at stake:
- Brand damage from AI-generated bias, misinformation, or toxicity
- Compliance violations under GDPR, HIPAA, and AI-specific laws
- Operational disruptions if pipelines are compromised or misused
The Future of AI Security Is Real-Time Adaptation
Enterprise security teams must expand beyond static controls and pre-deployment checks. What’s needed now is a dynamic, adaptive inference security stack—including:
- Deep content inspection (input + output scanning)
- Autonomous exposure management: (red-teaming, vulnerability assessment)
- Model-aware behavior monitoring (not just keyword filters)
- Hybrid deployment flexibility (SaaS + on-prem)
To get the full breakdown, download our white paper, Security Risks of Generative AI Inference, here.
