Rising AI Security Concerns in 2025: Is Your Data Safe?

The smarter AI becomes, the smarter hackers get. Is your data ready for the battle ahead?

AI tools like ChatGPT, DeepSeek, Claude, etc., have significantly made operations a lot easier for businesses around the world. These generative AI technologies can help with tasks like content creation, image design, emails, meeting scheduling, and even code development. But it is not always a walk in the park! As AI models have become the new backbone of modern business, they are also becoming prime targets for sophisticated, invisible threats. In the rush to scale AI, many organizations are skipping one critical step: security. This kind of slip-up doesn’t just lead to breaches it also breaks customer trust, invites legal trouble, and dents market confidence. This means organizations need a new kind of defense, one that secures not just systems, but intelligence itself.

In this blog, let’s unpack the real AI security challenges organizations face today and the best practices to stay protected, proactive, and AI-ready.

Top AI security risks in 2025 

Here are some of the major AI data security risks that every business should be aware of:

1. Data breaches 

AI thrives on data. Vast amounts of data are used for training and running AI models, which makes them a prime target for malicious actors. When unauthorized individuals gain access to confidential data from the AI system it results in exposure of personal information, intellectual property details, financial records or any other sensitive data. Nearly 87% of security professionals report that their organization has encountered an AI-driven cyber-attack in the last year! Source. This can happen through hacking, exploiting vulnerabilities in AI models or infrastructure, insecure APIs, misconfigured cloud storage, or even through model outputs that unintentionally reveal sensitive information. 

Real World Example: Over 3.75 million TaskRabbit user records were compromised when hackers used an AI-enabled botnet to launch a massive distributed denial-of-service (DDoS) attack on the platform. This attack resulted in the theft of personal and financial information, including Social Security and bank account numbers. The breach was so severe that TaskRabbit had to shut down its website and mobile app temporarily to contain the damage. Source

2. Adversarial attacks 

Adversarial attacks are deliberate attempts to deceive or manipulate artificial intelligence (AI) models by introducing specially crafted inputs known as adversarial examples that cause the model to make incorrect or unintended predictions. These manipulations are often imperceptible to humans but can cause AI systems to misclassify or malfunction.

Real World Example: Researchers manipulated Google object recognition AI  to missclassify a turtle for a rifle with some minor pixel modifications to training images. Such vulnerabilities could be used to bypass the facial recognition system in the Security Applications. Source

3. Data poisoning

Data poisoning is a cyberattack where the attacker manipulates or corrupts the training dataset used to develop a particular AI or ML model in a sector. This training data comes from various sources such as the internet, Government databases, and other third-party data providers. Large language models and deep learning models heavily depend on the quality and integrity of this training data for their models to function effectively. By intentionally inputting false or misleading information into these datasets, malicious actors can slightly or drastically change a model's behaviour. This reduces the efficiency and accuracy of AI models, creating serious risk factors, especially in industries such as health care, security, finance and autonomous vehicles. There are two most common types of data poisoning

1. Target data poisoning attacks: Here, the hackers manipulate AI model outputs in a specific way, while maintaining the model's general functionality. 
2.  Non-target data poisoning attacks: The hackers aim to degrade AI model performance broadly rather than targeting specific outcomes that create systematic vulnerabilities and compromises overall reliability. 

Real World Example: Microsoft’s AI chatbot Tay was designed to converse and learn from Twitter users. But within 16 hours of its launch, it was shut down due to posting offensive content. Malicious actors flooded it with offensive language and topics, teaching it to replicate such behaviour. Tay’s tweets quickly turned into racist and explicit messages, which led to it being shut down. Source

   4. Data leakage

Data leakage occurs when sensitive data is unintentionally exposed or made accessible to unauthorized parties, often due to negligence, misconfiguration, or human error. 

Here are the common types of data leakage

• Training data leakage: Data from the training dataset is unintentionally exposed by the model during inference.  
• Inference Attacks: Attackers extract confidential information by querying the AI model in specific ways. 
• Deployment Leakage: Data can be leaked due to improper security during the deployment of AI systems. 
• Pipeline Leakage: Data intercepted or mishandled during preprocessing, transfer, or storage

Real World Example: In March 2023, a bug in ChatGPT’s open-source library caused users to see other users’ personal data, including first and last names, email addresses, payment addresses, and partial credit card information. Source

   5. Lack of explainability

When AI models operate as “black boxes”, it becomes difficult to understand, audit or justify their decisions. For the users to trust AI, they need to know the model has arrived at the outcomes. The inability of the model to explain its decisions can hide vulnerabilities, making it harder to detect errors, biases or manipulations. Attackers may use this loophole to manipulate outcomes or evade detection, leading to mistrust and even harmful outcomes. 

Real World Example: In critical applications such as medical diagnosis, when an AI system is used to identify a condition but cannot explain how it arrived at that conclusion, doctors may find it difficult to trust the diagnosis. In case the AI is wrong, the patients might receive inappropriate treatment, leading to severe consequences.

   6. Supply chain risks

Supply chain risk in AI data security refers to the vulnerabilities introduced through third-party vendors, tools, data providers, cloud platforms, open source libraries, or infrastructure that an AI system depends on. These external components are often outside your direct control, making them potential entry points for cyberattacks, data leaks, or model manipulation.

Real World Example: In 2022, an open-source AI package on PyPI was discovered to contain malicious code that could steal environment variables, API keys, and credentials. Developers unknowingly using this dependency exposed their entire system to exploitation. Source

  7. Shadow AI

With the ease of access to AI tools and technologies, and the desire to increase productivity, employees or departments use unauthorized AI tools without organizational oversight or the approval of the IT or security teams. This is called Shadow AI. According to Microsoft, AI tools are an increasingly common part of the workflow, with 75% of workers using them. Of those workers, 78% are bringing their “own“ AI tools to work! Source. When employees feed sensitive company information or consumer data into external AI tools (like ChatGPT or other OpenAI models), it creates a wider attack surface related to AI data security, compliance, and organizational integrity.

Real World Example: Samsung employees accidentally leaked confidential information by using ChatGPT to review sensitive internal code and documents. As a result, Samsung decided to ban the use of generative AI tools across the company to prevent future breaches. Source

   8. Phishing Attacks

Attackers use AI to gather vast amounts of data from social media, public records and online activity to build detailed profiles of their targets. With this data, AI generates highly convincing, personalized messages tailored to the recipient’s interests, recent activities, or even mimicking the writing style of trusted contacts. AI enables attackers to automate and mass-produce unique phishing campaigns, targeting many individuals or organizations simultaneously.

Real World Examples: In 2024, attackers used deepfake video calls to impersonate a multinational firm’s CFO, tricking a finance employee to transfer approximately $ 25 million! Source

Regulatory & Compliance Challenges

• Overview of global regulations (GDPR, CCPA, HIPAA) impacting AI data security
• The need for auditability, transparency, and explainability in AI models
• How non‑compliance can undermine trust and invite legal exposure

AI Security Best Practices to Mitigate these Risks

Let’s explore some ways to mitigate these AI data security risks

1. Implement Data Handling and Validation 

Data is the foundation of every AI system - but also its biggest vulnerability! Implementing strong data handling and validation processes helps ensure your AI system is trained on clean, secure and reliable inputs. Poor handling or lack of validation can lead to biased models, breaches or even model manipulation. 

Here are things you can do:

• Always track the source of your data, including data from third-party data sources, to verify its authenticity.
• Remove duplicates and identify anomalies before training your system
• Enforce structured schemas to catch errors early in pipelines and employ strong encryption standards to protect sensitive information.
• Use data anonymisation or pseudonymization, particularly for personal or regulated data like Healthcare and financial records.

      2. Robust Model Validation and Testing

Before deploying an AI model, it’s essential to ensure it performs safely, ethically, and securely under real-world conditions. Pre-deployment validation helps identify and fix potential flaws like bias, vulnerabilities, or performance issues before they can be exploited or cause harm.

• Conduct adversarial testing using tools to simulate attacks.
• Perform fairness and bias audits to detect and correct skewed predictions.
• Benchmark models on edge cases and real-world scenarios.
• Run AI security regression tests to validate fixes and updates. 

3. Allow Only Safe Models and Vendors 

Evaluate the security practices of the third-party vendors and scrutinize the implementation of AI models for potential vulnerabilities. This reduces the risk of insecure components in the systems. 

Best Practices:

• Use role-based access control (RBAC) for model management tools.
• Require multi-factor authentication (MFA) for all devs and admins.
• Vet all vendors and third-party models or APIs for compliance and security certifications.
• Track and log all interactions with data and models.

4. Establish a Data Governance Framework

AI governance Framework acts as a rulebook to define who owns the data, how would can be used, where it resides, and what standards it must meet. This Framework is short compilers with data protection laws like GDPR, HIPAA, etc. It prevents data misuse or leakage by reducing security risks from poor data handling.

• Assign clear data owners and data stewards responsible for specific datasets.
• Data can be tagged based on sensitivity, which helps to determine what security and access controls are needed for each
• Policies can be implemented for how long data should be retained, when it should be anonymized, and who can delete it or export it. 
• Include guidelines on cross-border transfers, user consent, data management and minimization, and auditability.

5. Use AI-Driven Security Solutions 

Traditional cybersecurity tools have their uses, but they aren’t equipped well to handle all the challenges that AI systems introduce. 

• AI cybersecurity systems utilizes machine learning algorithms to analyse vast datasets and rapidly identify and adjust to new threat vectors and attack methodologies
• AI-driven solutions can automate threat detection across multiple sources and search for threats that have evaded initial detection and even automatically respond to incidents.
• Advanced AI security solutions are now using explainable AI techniques to give a transparent view of how AI navigates the decision-making process. 

6. Continuous Monitoring and Testing

AI models are not static. Even after deployment, they’re exposed to real-world data and user interactions that can change rapidly. Constant surveillance of AI applications is necessary to detect anomalies and potential issues in real time. By keeping a note of the KPIs, model performance fluctuations organizations can quickly identify irregularities which could potentially be a security breach or malfunction. 

Continuous monitoring and testing can

• Ensure real-time detection of data, drift, model degradation and security threats
• Track feature shifts, performance metrics and unexpected outcomes
• Detects and prevents adversarial attacks and model manipulation
• Keeps AI systems accurate, fair, and resilient in dynamic environments.

7. Zero Trust Architecture (ZTA) for AI Systems

Adopt a “never trust, always verify” model across your AI ecosystem.

• Treat every dataset, model, API, or user as potentially untrustworthy.
• Authenticate and authorize every access request, even inside the perimeter.
• Continuously validate the behavior of systems and users.

Final Thoughts: Is Your Data Safe in an AI World?

The truth is not always the truth! While AI unlocks enormous potential, it also introduces a new dimension of cyber risk that’s evolving fast. As we build smarter machines, we must also build stronger shields. The question is no longer "Can we build it?" It’s "Can we build it responsibly and securely?" Because we’re not just protecting data, we’re protecting decisions, reputations, lives, and the very trust that people place in intelligent systems. So, it’s time to shift from a “build fast and fix later” mindset to one that prioritizes proactive defence, responsible data practices, and continuous monitoring. 

In an era where AI decisions impact millions every click, diagnosis, recommendation, or transaction, security must be engineered into every layer: data, models, infrastructure, and operations. With our robust Generative AI services, Tredence helps enterprises build secure, scalable, and responsible AI solutions. Partner with Tredence to build AI systems you can trust

FAQ

1. What are common threats to AI systems?

The top risks include data poisoning, adversarial attacks, unauthorized AI (shadow AI), biased models, and AI model drift. These risks can lead to faulty decisions, data leaks, or compliance violations if not proactively addressed through secure design and ongoing oversight.

2. How can organizations protect their AI systems from cyber threats?

Organizations can protect their AI systems by securing data pipelines, implementing strong access controls, and regularly validating models against adversarial attacks. They should adopt MLOps for continuous monitoring, encryption for sensitive data, and conduct frequent security audits. Partnering with experts ensures AI is deployed with built-in defence mechanisms.

3. What privacy concerns are linked to AI systems?

AI systems often process vast amounts of personal and sensitive data, raising serious privacy concerns. These include unauthorized data collection, lack of transparency in how data is used, and potential re-identification of anonymized data. Without strong AI security measures like data governance and compliance measures, AI can unintentionally violate regulations like GDPR or misuse personal information.

4. What is the first step for a business to assess its AI security posture?

Start with a comprehensive AI risk assessment that reviews data governance, model robustness, compliance, and infrastructure. From there, build a roadmap that includes model validation, adversarial testing, and real-time monitoring. Tredence’s AI Consulting Services help you assess your current AI landscape and build a secure, scalable, and ethical AI foundation tailored to your industry.

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Editorial Team
Tredence