AI data security is no longer optional. Stanford’s 2025 AI Index Report, which found a steep escalation in AI-related privacy and security breaches, should serve to reiterate this reality for businesses. Moreover, almost 84% of all AI tools were found to have experienced some form of data breach, with more than 50% suffering a credentials theft, as more and more organizations rush towards rapid AI adoption without appropriate oversight measures implemented.
Consider this: Thales’ Data Threat Report for 2025 revealed that almost 70% of organizations now consider adoption of GenAI tools to be their top security concern, far outranking traditional trust and data integrity issues.
All this paints a portrait of a threat landscape evolving at a geometric rate, where organizations cannot afford to rely on traditional data security parameters alone. With AI systems introducing new attack surfaces, organizations must accept, adopt, and perfect AI data security as a core component of their enterprise resilience structure.
Read on to learn more about AI data security.
Key Risks in AI Data Security
Data Leakage & Exfiltration
Data leakage is one of the most immediate threats any AI system faces, with sensitive information consistently at risk of being exposed through AI interactions or generated outputs. While LLMs and AI agents are designed to ingest and process vast volumes of data consistently, the absence of strict guardrails can lead to unintentional leakages of proprietary datasets, customer information, or confidential IP.
Similarly, exfiltration risks are particularly a concern in agentic AI systems, where AI can call APIs, retrieve files, or interact autonomously with third-party tools. A malicious actor may exploit prompt injection techniques to redirect an AI agent towards leaking trade secrets. A 2025 Black Hat demo illustrated exactly this, with a simple injection leading to credential theft and a downstream system compromise.
However, none of these resemble traditional breaches, as instead of large data dumps, sensitive data snippets bleed out subtly and accumulate risk over an extended period, designed to be this way to minimize chances of detection.
Model Poisoning & Integrity Threats
AI systems will only ever be as reliable as the data they’re being trained on. However, malicious or manipulated data can be injected into the training datasets and pipelines to skew the outputs or create hidden backdoors. This can lead to biased decision-making, misinformation in outputs, or even embedded vulnerabilities that can be exploited later on through specific triggers. The attack surface for such vectors has only expanded with modern businesses relying more and more on Retrieval-Augmented Generation (RAG) and shared datasets.
Integrity threats go further than just deliberate poisoning. Adversarial inputs can be crafted to confuse or manipulate models and cause erratic outputs. This can lead to customer-facing automated services such as chatbots or AI models providing dangerous advice. On an operational level, this means automated workflows executing faulty decisions on scale.
To mitigate such threats, organizations must adopt a secure and reliable MLOps pipeline with the appropriate data provenance checks, version control, adversarial testing, and continuous monitoring to ensure organizations do not have to worry about model outputs’ reliability and accuracy.
Privacy & Regulatory Compliance Risks
More than just technical vulnerabilities, AI systems can also expose organizations to various privacy and compliance risks. Various data privacy regulations, such as the GDPR, HIPAA, CPRA, GLBA, PIPEDA, etc, in addition to AI laws such as the EU’s AI Act, place several strict requirements and conditions on how personal and sensitive data can be collected, processed, retained, and most importantly, used to train AI models. In the face of such requirements, numerous organizations still struggle to maintain a proper inventory of their datasets, with little or no insight into which datasets are flowing into their AI training pipelines.
There are countless instances of some of the most recognizable organizations globally receiving multi-million dollar fines and financial penalties for non-compliance with some of the aforementioned regulations’ requirements. More than just the financial implications of such fines, they cause irreparable harm to an organization’s reputation in the market and with its customers.
Hence, it is vital for all organizations to adopt and embed a compliance-by-design framework into their AI strategies, leveraging tools such as DSPM that guarantee continuous monitoring, alongside legal-technical partnerships that ensure extensive adherence to the evolving regulatory landscape.
Securing the AI Data Lifecycle
Data Collection & Classification
The foundation of any organization’s measures to protect its AI data is to have complete visibility and knowledge of what data is being collected and from where. Far too often, AI models are being trained on datasets obtained from third parties or sources via shadow AI practices. Some of these origins lack proper vetting, creating several risks related to data accuracy, intellectual property infringement, and regulatory non-compliance.
Through data classification, organizations can ensure all their data assets are tagged per the sensitivity levels, with differential protections applied based on the nature of each asset. Tools like DSPM can automate such classification at scale, giving organizations a complete overview in real-time of their entire data infrastructure.
Data Storage & Access Control
Once an organization has clarity on what data it collects, it must then prioritize how it is stored. At the bare minimum, organizations must have encryption and access control processes in place to limit malicious or accidental exposure. In contrast to traditional databases, AI training data usually consists of mixed data types and is at scale. This makes them a highly lucrative target for potential attackers. Hence, an organization’s encryption framework should cover data at rest, in transit, and in use.
Access control frameworks such as role-based access control (RBAC) and attribute-based access control (ABAC) ensure only authorized personnel and systems can have access to the sensitive data assets. Done effectively, it would ensure only people or systems that should have access to a particular asset end up with access, along with the relevant permissions to change, modify, or alter them. Adding zero-trust architecture, where every access request is considered potentially hostile, adds a degree of proactiveness to an organization’s AI security posture.
Data Processing & Training
It is at the processing and training stage that the data integrity and model reliability are at most risk. It is also at this point where a malicious actor is most likely to inject poisoned or mislabeled data that would skew the AI model’s behavior in a manner that would allow manipulated outputs. Researchers at the University of Pittsburgh demonstrated exactly this, in addition to just how precisely entire attack chains can be triggered via highly precise prompts.
This should reiterate the importance of securing the entire training pipeline, from the ingestion phase to the deployment. This would include verification of dataset origins, hashing datasets for tamper detection, and monitoring for anomalies across multiple vectors during training phases. Both as a regulatory requirement in some instances and a best practice in others, organizations should maintain extensive audit logs to record each training iteration for both future operational introspection and regulatory expectations.
Data Sharing & Outputs
For most enterprises, the reputational risks of lapsing AI data security are likely to occur at this juncture. Even the most well-secured models can end up disclosing sensitive data in their outputs unless appropriate guardrails are built into them. Think of an AI-enabled chatbot that surfaces fragments of internal documentation or user records when fed with a cleverly crafted prompt.
Organizations can leverage data security posture management (DSPM) as a means to enable secure data sharing, as it offers proactive tagging and classification of sensitive data across various cloud environments, while also providing visibility into who has access to it, how it's being used, and potential blind spots related to the current data security posture. This can be further strengthened by output monitoring tools such as AI firewalls that filter responses for potential policy violations, toxicity, or compliance breaches at every AI event instance.
AI-Specific Security Controls
AI Firewalls & Guardrails
As stated earlier, traditional tools, including firewalls, are ill-equipped to protect the interactions between users and AI models. AI firewalls, on the other hand, were designed to address this very issue, as they sit between the user and the model to inspect, filter, and block malicious or non-compliant prompts and outputs at every AI event instance.
Additionally, policy guardrails can also be embedded directly into the AI system’s behavior, allowing organizations to enforce redaction rules, prevent outputs that reference sensitive datasets, and block certain flagged responses. Furthermore, models can be outright restricted from generating unauthorized advice or disclosing confidential information.
Red-Teaming & Evaluation
No AI system can be deemed or considered secure without continuous adversarial testing. Red-teaming in the AI context enables probing of the AI models for a barrage of weaknesses. This includes testing whether prompt injections can bypass controls, whether the system can be manipulated into leaking sensitive data, or whether adversarial inputs can cause unsafe or unexpected behaviors. Moreover, such red-teaming exercises are now becoming a requirement under various AI legislations being drafted globally.
More importantly, model evaluations do not end after the model deployment. Models must be assessed for ongoing performance along with regular safety checks that include bias assessments, hallucination detection, and monitoring for model drift. Doing so ensures the security controls remain effective as both threats and AI capabilities evolve.
Agent Governance
Agentic AI was expected to, and has had, a profoundly revolutionary impact on businesses. It is one of, if not the most, effective applications of GenAI capabilities for enterprises. It can autonomously call APIs, retrieve documents, and execute tasks on behalf of users, with comprehensive workflows giving it both the freedom and flexibility to perform operations at an unprecedentedly efficient rate. However, without proper governance structures, these agents can easily become conduits for privilege escalation, data exfiltration, or critical operational disruption.
Agent governance involves defining clear permission boundaries. This is easier said than done, considering the extensive capabilities of such agents. Still, agents must only be granted the least possible privilege necessary to perform their tasks, while also being consistently monitored for anomalous behaviors, particularly excessive API calls and unauthorized data retrieval.
Finally, policy engines must also be considered, which would dictate what the agents can do. This includes various aspects of agentic AI operations, ranging from restricting access to sensitive datasets to limiting third-party integrations. This would ensure fluency of workflows without the possibility of potential AI data security gaps.
How Securiti Can Help
For organizations, AI data security will continue escalating as both an operational and strategic challenge. Most AI applications rely on extensive access to large volumes of data to continue delivering expected value, and users and partners providing this data expect it to be appropriately protected at all times. Hence, ensuring appropriate AI data security represents a significant challenge.
This is where Securiti can help.
Securiti’s Gencore AI is a holistic solution for building safe, enterprise-grade generative AI systems. This enterprise solution consists of several components that can be used collectively to build end-to-end safe enterprise AI systems and to address AI data security risks across various use cases.
This enables an incredibly effective yet simplified enterprise AI system through comprehensive data controls and governance mechanisms that mitigate all identifiable risks proactively.
It can be further complemented with DSPM, which provides organizations with intelligent discovery, classification, and risk assessment, marking a significant shift from a reactive data security approach to proactive data security management suited to the AI context.
Request a demo today to learn more about how Securiti can help your organization optimize its DataAI security.
Frequently Asked Questions
Some of the most frequently asked questions related to AI data security include:
