Use Case

Data Security for AI

AI is Changing How We Work

AI has enormous potential, from traditional AI and machine learning, to new use cases leveraging generative AI and LLMs. More than 50% of organizations expect to use AI in the next two years, and 40% will increase their investments in AI overall because of advances in gen AI.

The AI Security Challenge

Security is critical to the success of AI, but is also one of the main blockers. Cybersecurity is the number one risk organizations are looking to mitigate, and unless they can solve it, the adoption of AI tools in the enterprise will likely be halted altogether.

AI security and privacy challenges include biased decision making, inaccurate recommendations, outlier misinterpretation, privacy risks, legal/ethical concerns, and trust issues (i.e. AI hallucinations and confabulations). To solve these challenges, organizations must have a governance strategy and proper data security tools for AI.

Data Security for Traditional AI/ML Pipelines

Immuta provides data security for use cases such as fraud detection, predictive maintenance, next best offer, risk analysis, and more. By acting as a centralized mechanism for managing data governance, Immuta helps you improve MLOps workflows and tackle complex use cases.

Data Security for Emerging Gen AI Use Cases

Immuta's advanced data classification and tagging help you govern the data fed into LLMs, so sensitive data can be easily identified and masked as necessary. By enforcing access control before data is loaded into the model, users can leverage Immuta’s PETs such as k-anonymity to insert noise into data sets, so that AI outputs cannot inadvertently reveal PII or other sensitive information.


Secure MLOps at Swedbank

Swedbank leveraged Immuta to improve its MLOps and tackle fraud detection challenges.

“Implementation of machine learning ops using the platform has enabled shorter development cycles, which has resulted in shorter times-to-market. It also has the ability to detect and act on new patterns related to fraud, suspicious behavior, and so on, as well as providing us with the ability to utilize green cloud providers to minimize environmental impact.”

Vineeth Menon Head of Data Lake Engineering
Immuta Features

The Immuta Advantage

Immuta’s data tagging and classification, access control, and data usage monitoring provide the necessary data security capabilities to protect against many of OWASP’s top 10 emerging threats for AI.

Training Data Poisoning

Avoid LLM training data tampering that introduces vulnerabilities or biases which compromise security.

Sensitive Information Disclosure

Mitigate the risk of private LLMs inadvertently revealing confidential data in responses.

Unauthorized Model Use

Prevent unauthorized use of purpose-built models out of context (e.g., employees using the HR LLM for PR purposes or vice versa).

LLM Supply Chain Vulnerabilities

Monitor the usage of data throughout the data supply chain, from ingest to model creation, to model output.

Excessive LLM Autonomy

Separate policy from platform to avoid LLM-based systems being granted excessive data access rights and autonomy.

Model Theft

Avoid unauthorized access, copying, or exfiltration of proprietary LLM models.


Get the Most Out of
AI Technologies

Without Immuta With Immuta
ML initiatives stall out as use cases grow
Easily run MLOps securely at scale

Unable to anticipate and plan for AI risks
Mitigate common AI data risks

Lack of visibility into potential AI output security violations
Monitor generative AI outputs for security

Unaware of potential AI security blindspots
Safely use AI across the enterprise

Frequently Asked Questions

Why do I need AI security?

As artificial intelligence (AI) and machine learning (ML) continue to evolve, they present businesses with wider range of data use cases. While this offers exciting new opportunities for data-driven insights, it also subjects data to additional risks, like data privacy shortcomings, model exploitation and misconfiguration by bad actors, and training data poisoning, to name a few. Implementing AI security is a crucial step in the development and application of AI tools, ensuring that the data involved is sufficiently protected against these risks. Creating and implementing AI security is a critical step in the development of any AI tool.

What are some common generative AI security risks?

Some of the most common generative AI security risks include:

  • Advanced Phishing: Using AI or deepfakes to generate more believable phishing emails, messages, etc.
  • Data Privacy: Exploiting nascent models that leverage personal data
  • Model Inversion: Extracting personal information from data subjects by observing AI model outputs
  • Model Poisoning: Distorting AI models during training by injecting false information
  • Model Evasion: Generating incorrect output from a model by perturbing input data
What is AI data analytics?

AI data analytics refers to leveraging tools that automate the data analytics process. This reduces manual work, enabling businesses to efficiently analyze large volumes of data. Examples of AI data analytics include using predictive analytics to examine trend data and forecast outcomes, using AI tools to generate and maintain dashboards and reports, and engaging AI in fraud detection and prevention measures to ensure data quality.

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