AI Security Risks in 2026: Is Your Data Really Safe?

The smarter AI becomes, the smarter attackers get. Is your data ready for what comes next?

Tools like ChatGPT, DeepSeek, and Claude have changed how businesses operate, from automating content and code to scheduling and analysis. But as AI becomes the backbone of modern enterprise, it has also become the most attractive target. Organizations scaling AI fast are skipping one critical step: security. That gap does not just cause breaches; it destroys customer trust, invites regulatory penalties, and shakes market confidence.

According to Gartner, worldwide end-user spending on information security is projected to reach $212 billion in 2025, driven largely by rising threats and the expanding use of generative AI by both enterprises and attackers. That number is not just a market signal; it is a warning. (Source)

This blog breaks down the real AI cybersecurity threats organizations face today, what they actually look like in practice, and the specific steps needed to build a responsible generative AI ecosystem that holds up under real-world pressure.

What Are the Biggest AI Security Risks in 2026?

AI security is not the same as traditional cybersecurity. It covers threats specific to AI models, training data, inference pipelines, and deployment environments. Here is what every organization needs to watch.

1. Data Breaches

AI systems run on data, which makes them a prime target. When unauthorized access hits an AI environment, it can expose personal information, financial records, intellectual property, and model configurations. These generative AI security risks range from insecure APIs and misconfigured cloud storage to exploited model vulnerabilities and outputs that accidentally surface sensitive training data.

Gartner predicts that by 2027, more than 40% of AI-related data breaches will result from improper cross-border use of generative AI, a direct consequence of governance gaps outpacing deployment speed. (Source)

Real-world example: In March 2023, a bug in ChatGPT's open-source library caused users to see other users' personal data, including names, email addresses, payment details, and partial credit card numbers. OpenAI confirmed the incident and temporarily took the service offline. (Source)

2. Adversarial Attacks

Adversarial attacks are deliberate attempts to manipulate AI models using specially crafted inputs that cause incorrect predictions. These manipulations are often invisible to humans but can push AI systems to misclassify, malfunction, or behave in dangerous ways. In LLM security, adversarial inputs are particularly dangerous because they can bypass safety guardrails, manipulate model outputs, and expose vulnerabilities that are nearly impossible to detect without continuous testing. 

Common adversarial attack types:

• Evasion attacks: Manipulate inputs at inference time to fool the model
• Model inversion: Reconstruct training data from model outputs
• Membership inference: Determine whether a specific record was in the training set

3. Data Poisoning

Data poisoning is a cyberattack where an attacker corrupts the training dataset used to build an AI or machine learning model. Since LLMs and deep learning models depend heavily on training data quality, injecting false or misleading information can change a model's behavior in subtle or catastrophic ways, particularly in healthcare, finance, security, and autonomous systems.

Real-world example: Microsoft launched AI chatbot Tay on Twitter in 2016. Within 16 hours, malicious users flooded it with offensive language, teaching it to replicate racist and explicit content. Microsoft shut it down the same day. (Source)

4. Data Leakage

Data leakage happens when negligence, misconfiguration, or system design flaws unintentionally expose sensitive data to unauthorized parties. In AI environments, leakage can happen at multiple stages of the pipeline.

Leakage types and where they occur:

5. Prompt Injection

Prompt injection is currently the number one vulnerability on the OWASP LLM Top 10. (Source) An attacker embeds malicious instructions into an input or external content that the AI model processes, causing it to ignore its original instructions and execute the attacker's commands instead.

In direct prompt injection, the user overrides system instructions. In indirect prompt injection, malicious prompts are hidden inside documents, web pages, or data that the AI retrieves and processes. As LLMs gain access to tools, APIs, and databases, prompt injection becomes a gateway to full enterprise compromise.

Real-world example: Google's security research identifies three traits of indirect prompt injections on public webpages: altering AI conversational tone, hijacking functions via adversarial instructions, and using hidden SEO code to prioritize businesses or crash processors. These attacks typically aim to manipulate web data and large language model responses. (Source)

6. Supply Chain Risks

Supply chain risk in AI refers to vulnerabilities introduced through third-party vendors, open-source libraries, data providers, cloud platforms, and infrastructure that an AI system depends on. These components sit outside your direct control, making them potential entry points for attacks or silent model manipulation.

7. Shadow AI

Shadow AI occurs when employees or departments use unauthorized AI tools without IT or security team approval. McKinsey's 2025 State of AI report found that 47% of organizations have already experienced at least one negative consequence from generative AI use, with employees often using AI tools far more than their leaders realize. (Source) When sensitive company data or consumer records get fed into external AI tools, it expands the attack surface and creates serious compliance exposure under GDPR, HIPAA, and CCPA.

Real-world example: Samsung employees leaked confidential source code and internal documents by pasting them into ChatGPT for review. Samsung subsequently banned generative AI tools across the organization. (source)

8. Phishing and Deepfake Attacks

Attackers now use AI to analyze social media, public records, and behavioral data to build detailed target profiles and generate hyper-personalized phishing messages. These messages mimic trusted contacts, replicate writing styles, and bypass traditional email filters. Deepfake video and audio capabilities take this further.

Forrester's 2025 threat report explicitly identifies deepfake technology as a serious threat to enterprise security, with biometrics vendors expected to allocate 20 to 30% of R&D budgets to deepfake detection. (Source)

9. Regulatory and Compliance Challenges

AI compliance is no longer optional. Global regulations now impose direct obligations on how organizations build, deploy, and govern AI systems.

Non-compliance does not only invite fines. It exposes organizations to reputational damage, loss of enterprise contracts, and legal liability when AI-driven decisions cause harm. Auditability, explainability, and documented governance are no longer optional features; they are regulatory requirements.

Top 7 AI Security Best Practices to Mitigate These Risks

Gartner predicts that by 2028, 25% of all enterprise generative AI applications will experience at least five minor security incidents per year, up from just 9% in 2025. (source) Here are the seven practices that security-mature organizations are already building into their AI programs.

1. Start With Strong Data Handling and Validation

• Verify the origin of every dataset, including third-party sources, before training begins.
• Remove duplicates, flag anomalies, and apply encryption across sensitive inputs.
• Utilize data anonymization for regulated categories like healthcare and financial records.
• Enforce controls at the pipeline level by following AI data guardrails, particularly for agentic systems.
• Establish clean inputs as the essential foundation for downstream AI security decisions.

2. Test Your Model Before It Goes Live

• Treat any model that has not undergone adversarial testing as a significant liability.
• Execute red team simulations specifically targeting prompt injection scenarios.
• Conduct thorough audits for fairness and bias, and perform benchmarking against edge cases.
• Implement regression testing following every model update.
• Address pre-deployment validation gaps to prevent negative consequences, which McKinsey reported have already affected 47% of organizations using generative AI. (Source)

3. Build an AI Governance Framework

• Define data ownership, usage, location, and required standards through an AI governance framework.
• Assign data owners and classify datasets based on sensitivity.
• Establish retention and deletion policies and document rules for cross-border data transfers.
• Operationalize principles across the AI lifecycle using the NIST AI RMF and the July 2024 Generative AI Profile (NIST AI 600-1). (Source)
• Recognize governance as a critical component for scaling responsible AI rather than just a compliance requirement.

4. Control Who Gets Access and From Where

• Mitigate supply chain attack risks originating from third-party vendors, open-source libraries, and unvetted APIs.
• Enforce role-based access control (RBAC) across all model management environments.
• Require multi-factor authentication for every developer and administrator.
• Vet all vendors for security certifications and log every interaction with data pipelines.
• Use access control as the main defense against supply chain compromise and unauthorized tool usage.

5. Use AI-Driven Security Tools, Not Just Traditional Ones

• Standard cybersecurity tools are insufficient for catching AI-specific threats like prompt injection, LLM security gaps, or adversarial inputs.
• Gartner projects global information security spending to reach $212 billion in 2025, with an increasing focus on AI-native detection tools to address specialized attack vectors. (Source)
• AI cybersecurity platforms leverage machine learning to analyze behavioral patterns, detect novel threats faster than rule-based systems, and automate incident response.
• Explainable AI techniques should be paired with these tools to ensure that security decisions are auditable and defensible for regulators.

6. Monitor Continuously After Deployment

• Implement real-time monitoring for data drift, model degradation, anomalous outputs, and performance threshold breaches to address silent changes in model behavior.
• Leverage MLOps services to automate retraining pipelines and version control, ensuring models remain accurate, fair, and resilient.
• Recognize that security is an ongoing process that begins at go-live rather than ending there.
  

7. Apply Zero Trust Security Across Your AI Environment

• Operate on the core principle of "never trust, always verify" for every dataset, model, API, and user interaction.
• Enforce least-privilege access to prevent compromised components from escalating throughout the entire environment.
• Apply specific zero-trust controls at the agent-tool boundary to secure AI agent deployments.
• For organizations deploying AI agents, AI agent security requires additional zero trust controls at the agent-tool boundary.

Securing AI with Tredence

Vulnerabilities within artificial intelligence systems often remain hidden until they cause data breaches, regulatory non-compliance penalties, or protracted periods of algorithmic degradation. By the point of detection, the resulting organizational impact is frequently substantial and irreversible.

To prevent these risks from materializing, Tredence partners with organizations to implement proactive defenses:

• Runs AI maturity and risk assessments to identify gaps before deployment begins
• Designs technical architecture with security guardrails, access controls, and privacy-by-default built in
• Enforces responsible AI and AI governance frameworks aligned with NIST AI RMF and EU AI Act requirements
• Deploys AI data guardrails and end-to-end observability with audit logs and KPI dashboards
• Uses MLOps and LLMOps for continuous model monitoring, drift detection, and version control
• Delivers AI model security through built-in compliance verification before any model goes to production

When Mars partnered with Tredence to scale generative AI enterprise-wide, the engagement standardized LLMOps, enforced responsible AI compliance across all deployments, and strengthened risk controls without slowing down innovation. (Source)

AI Security Best Practices: Where to Start

Every AI system your organization runs today carries risk, in the data it learns from, the models it deploys, and the decisions it makes at scale. The question is not whether threats exist. It is whether your defenses are keeping pace.

Getting ahead of generative AI security risks requires more than awareness. It requires action built into every layer of your AI program. Tredence's Generative AI services assist enterprises in building responsible AI that is secure, compliant, and production-ready from day one. Connect with our team to assess your AI security posture and build a roadmap that holds up under real-world pressure. 

FAQ

1. What is AI security and why does it matter for my business? 

AI security protects your models, training data, and inference pipelines from threats like prompt injection, data poisoning, and adversarial attacks. Without it, a single vulnerability can compromise your decisions, expose sensitive data, and trigger regulatory penalties across your entire AI environment.

2. How do I know if my organization's AI systems are actually secure? 

You can start with a comprehensive AI risk assessment that reviews your data governance, AI model security, access controls, and compliance alignment with frameworks like NIST AI RMF. From there, you build a roadmap covering adversarial testing, continuous monitoring, and responsible AI practices tailored to your industry.

3. What is the difference between AI security and cybersecurity? 

Cybersecurity protects your networks and systems. AI security specifically addresses threats unique to AI, including LLM security gaps, generative AI security risks, data poisoning, and model drift that traditional cybersecurity tools are not equipped to detect or prevent.

4. What is an AI governance framework? 

An AI governance framework is a structured set of policies that defines who owns your data, how it can be used, and what compliance standards it must meet. It keeps your AI systems aligned with regulations like GDPR, HIPAA, and NIST AI RMF while reducing security and compliance risks at scale. 

5. What is Shadow AI, and why is it a risk? 

Shadow AI refers to the use of artificial intelligence tools, platforms, or browser extensions by employees without the knowledge, approval, or oversight of a company’s IT and security teams. It typically occurs when employees use unsanctioned tools to increase their daily efficiency. 

6. How does zero trust security apply to AI systems? 

Zero trust security AI assumes that nothing inside or outside your environment should be trusted by default. Every model, dataset, API call, and user request gets verified before access is granted. For organizations running agentic AI or managing LLM security at scale, this principle matters because a single compromised component can quietly move through your entire system if there are no verification checkpoints stopping it.