Key Findings on AI Data Security Risks
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The most critical AI security risks include harmful confabulation (misleading outputs), adversarial attacks, unintentional data exposure through oversharing, broader data leakage incidents, and lack of visibility in AI systems. Each of these introduces serious legal, financial, and operational risks across businesses.
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AI data security risks are increasingly costly and complex. When breached, AI systems expose larger volumes of sensitive data and often remain undetected longer, driving the average breach cost 21% higher than conventional systems.
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Mitigating AI security risks requires a proactive, layered defense strategy tailored to data, model, and infrastructure levels—reactive approaches are costlier and less effective.
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Unresolved AI data security challenges are actively slowing digital transformation. Numerous companies have paused or limited AI adoption due to unmitigated risks, underscoring the importance of embedding security from the design phase.
Today, artificial intelligence (AI) supports almost every modern industry and data-centric task, from consumer service chatbots and fraud detection systems to predictive maintenance and real-time diagnostics in healthcare. However, the rapid adoption of AI has significantly increased the attack surface, as intelligent systems become valuable targets for cyberattacks by processing highly sensitive, proprietary, and personal data. A 2025 Wiz.io analysis highlights that organizations increasingly report AI data security incidents, particularly in production environments, pointing to a rising trend of unauthorized data access, prompt injection, and model misuse as AI adoption expands. These incidents include unauthorized/illegal data access, AI model theft, injection attacks, and data leaks across large language models (LLMs).
A report by IS Partners shows that 92% of healthcare organizations experienced at least one cyberattack in the past year, emphasizing the need of data protection in sectors handling sensitive information. Perception Point 2024 report highlights a 24% increase in cyberattacks per user and a rise in phishing and business email compromise attacks. Focusing on data leaks in AI, these incidents represent one of the most alarming risks. For example, LLMs like Google's Gemini or OpenAI's ChatGPT have demonstrated how easily user queries could unintentionally expose sensitive data. In one notable 2023 case, OpenAI's API allowed certain users to view titles from other users' chat histories, an exposure of user-generated content that, while not a training data leak, still raised concerns about privacy and system oversight in AI deployments.
In addition, attackers now use adversarial examples, model inversion attacks, and training data poisoning techniques—all capable of bypassing conventional cybersecurity defenses by specifically targeting AI vulnerabilities. The new AI-assisted features further amplify the threat potential of malicious tools like Darcula, enabling even non-technical users to effortlessly create tailored phishing pages with multi-language support and automated form generation. Model theft via frequent querying allows rivals to replicate a proprietary AI model's behavior, undermining years of R&D and exposing companies to both competitive and security risks.
Why Is It Important to Understand AI Security Risks Early?
Sensitive data—such as personal health records or financial information—is now being processed by AI at an unprecedented scale. When AI security fails, the consequences can be far-reaching for both individuals and organizations, and often costly. A SentinelOne 2025 analysis highlights the severity of adversarial manipulation and data leakage risks in AI systems, noting that these incidents challenge the effectiveness of conventional security infrastructures. These often constitute violations of strict data regulations like HIPAA in the United States and GDPR in the EU. The consequences can include legal fines, loss of customer trust, and significant long-term damage to brand reputation.
One more reason to examine thoroughly AI security risks is their potential to delay or even stop digital transformation processes in a company. Companies that wait until after deployment to handle hazards can find themselves compelled to costly retrofits. These reactive measures are less effective, more expensive, and slower than starting security from the start.
Top AI Data Security Threats to Watch in Your Organization
Specifically with LLMs, AI systems introduce new degrees of automation and efficiency, but they also create new security risks. These challenges are not merely hypothetical; real-world incidents across various sectors are already resulting from them. In the remainder of this section, some of the most common AI data security threats will be described.
AI oversharing
AI oversharing occurs in situations in which an AI assistant accesses and generates information that, while legitimately available to it, is inappropriately or overly accessible to the user, often due to lax access control or contextual boundaries. For instance, a model integrated into a corporate knowledge base might reveal internal HR records during unrelated queries.
AI hallucinations
AI hallucinations refer to false outputs produced by AI that seem convincing but are factually incorrect. In regulated sectors, this risk becomes especially serious. According to the 2025 survey, LLMs frequently generate factual inaccuracies in sensitive domains, especially when reasoning over domain-specific information like medical content, due to reasoning limitations and knowledge boundaries built into their training. Although these outputs might not involve data theft, hallucinations can influence judgments in legal systems, finance, or healthcare, potentially causing serious harm. Misdiagnoses, financial fraud, or disinformation can result from such outputs. Companies must manage the risks related to hallucinations with the same level of seriousness as they treat security breaches.
AI data leakage
AI data leakage, as one of the most pressing concerns in AI technologies, occurs when models expose sensitive content, which may include fragments of their training data, system-level implementation prompts, API metadata, or augmentative data sources used during fine-tuning or deployment. A typical cause of this is inadequate differential privacy during training or poor data filtering. According to Perception Point's 2024 research, the growing risk is that LLMs may expose sensitive financial or personal information in their outputs, especially in the absence of strong access controls, data validation, and prompt injection defenses. This issue directly compromises compliance with GDPR, HIPAA, and other privacy laws.
Adversarial AI attacks
Adversarial attacks target the processing logic of a model using carefully crafted inputs. To humans, these inputs seem natural, but they confuse the AI algorithm. For example, an AI-powered self-driving car might misclassify a slightly altered image of a stop sign, potentially leading to accidents. In the context of LLMs, adversarial inputs often take the form of prompt injection attacks, where malicious instructions are embedded into seemingly innocent prompts to alter the model’s behavior. For example, an attacker could craft a message that causes an AI assistant to leak sensitive internal data, generate harmful content, or override safety protocols. These manipulations are difficult to detect with conventional input validation mechanisms, making adversarial attacks among the most challenging threats to mitigate in generative AI systems.
Lack of visibility and audit trails
Without appropriate visibility into AI systems, organizations face challenges in identifying errors, complying with regulatory requirements, and enhancing model safety.
This is largely because many AI models, particularly complex ones like deep neural networks, lack transparency in their decision-making processes, making it difficult for humans to interpret outcomes or detect potential failures and security vulnerabilities. Reconstructing what went wrong becomes especially challenging if a model discloses sensitive information or rejects a legitimate request. Clear audit trails for AI decisions are mandated by ISO/IEC 27001 and the EU AI Act.
How to Mitigate Against AI Data Security Challenges
Reducing commented issues requires a combination of policies, new technologies, and procedures. It is important to emphasize that a reactive approach is not sufficient to address these problems. Organizations must adopt layered defense strategies tailored to the data, model, and infrastructure levels. Early implementation of efficient techniques is essential in this context.
Data masking
One of the most important mitigation techniques is data masking. It protects sensitive information by replacing real values with artificial ones. For example, names and account numbers can be hidden in data used for AI training. This guarantees that even if the dataset is accessed, personal identifiers are not exposed. A complementary technique is differential privacy, which instead introduces statistical noise into data or model training processes to mathematically guarantee that individual records cannot be inferred. Recent research shows that differential privacy mechanisms can reduce the risk of identifiable data leakage during the training of LLMs. Vu et al. (2024) show that DP-protected models mitigate membership inference attacks in federated learning settings without severe utility degradation, while Yu et al. (2024) show that private synthetic instruction generation maintains high alignment performance compared to real instruction data.
Model watermarking and fingerprinting
Model watermarking embeds invisible ownership signals into AI outputs or model architectures to establish provenance and support accountability. While traditionally researched as a means to detect unauthorized duplication or theft of model functionality, recent discourse has shifted toward using watermarking to identify AI-generated content, authenticate source material, and flag misuse, specifically in the context of misinformation or copyright infringement. However, such solutions currently have limited enforcement capability against large-scale reproduction of modern transformer or diffusion models. Therefore, watermarking should be viewed as a supportive but not comprehensive tool in the broader strategy of model governance and IP protection.
Input sanitization
Input sanitization can be useful but with limited defense against prompt injection attacks, which occur when malicious instructions are embedded in user inputs to hijack model behavior. However, according to IBM, “the only way to prevent prompt injections is to avoid LLMs entirely,” showing the limitations of input-based defenses alone. As an alternative, leading security recommendations, including those from NVIDIA and TLDRsec, recommend treating all LLM outputs as potentially untrusted. This includes inspecting and sanitizing outputs before triggering downstream services. Organizations should enforce strict parameterization of plugin templates. In addition, all external calls initiated by LLM responses should follow the principle of least privilege. While input sanitization should remain part of a multi-layered strategy, organizations are advised to assume some degree of injection success and focus on limiting the scope and impact of compromised interactions. According to a 2024 paper by researchers from the University of California, at least one type of prompt injection could be used against 62% of the commercial LLMs tested, exposing private data and bypassing safety measures. To keep a model secure, sanitization filters must be regularly updated to counter new injection techniques.
Output auditing
Output auditing is another important mitigation strategy. Sometimes, AI systems generate offensive or expose private and sensitive content. If a company ignores such a threat, the legal risks associated with producing undesirable material increase significantly. This risk can be partially reduced by using tools that scan AI outputs for sensitive words or data. Natural language classifiers and pattern recognition techniques enable these tools to detect problematic content and prevent it.
Zero-trust
Finally, companies can gain meaningful benefits from implementing zero-trust architectures. This approach treats every input and access request as untrusted by default. It forces systems to verify each request individually before granting access, effectively surrounding AI systems with zero-trust security layers. This includes request validation, access restrictions, and authentication. According to a 2024 report, companies that implemented zero-trust models experienced 59% fewer successful AI intrusions compared to those relying on conventional defenses.
How Knostic Can Help You Avoid AI Data Security Threats?
Knostic’s platform, focused on GenAI security, is designed for organizations to ensure that users cannot access (or infer) information outside their true need-to-know boundaries. Instead of securing information solely through groups and accounts, Knostic first determines what a user is authorized to access and then surveys all the information, data, and supplemental context available within the organization. This approach offers major advantages:
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A smaller and more focused remediation and discovery surface
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Faster time to act on critical data exposure risks
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A formalized framework to break down the challenge of securing an entire data ecosystem into manageable and actionable pieces
It is also good to know that seamless integrations of the Knostic solution with OpenAI, Azure, Anthropic, and other leading AI platforms guarantee safe and controlled deployments.
What’s Next?
As Generative AI grows, so does the importance of AI data security. Companies must begin managing risk actively rather than passively. This involves not only protecting the models, endpoints, and supply chains that feed and use AI, but also safeguarding the data itself. It is advised to always be prepared for incidents, including simulating AI-driven breaches as part of security planning.
Quoting Tal Zamir, CTO of Perception Point: "AI security is not just about stopping threats but proving trust. Without trust, adoption stalls."
FAQs
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What are the biggest AI data security risks today?
AI data leakage, prompt injection, model theft, adversarial attacks, and insufficient logging are top concerns.
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How do prompt injection attacks work?
They involve crafting user inputs that cause models to behave in unsafe or unexpected ways.
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Can AI systems leak training data?
Yes. Improper training or bugs may lead to data regurgitation.
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How can companies prevent AI data leakage?
Regular audits, access restrictions, zero-trust design, and use of privacy-preserving technologies.