AI innovation continues to impress, and in 2025, we’re looking ahead at the next big thing — Agentes de IA. These intelligent systems are emerging as the next transformative software layer to revolutionize businesses’ operations, empower employees, streamline workflows, and drive productivity to new heights. But what exactly are AI Agents, and how do they differ from the IA generativa (GenAI) y modelos de lenguaje grandes (LLM) we’ve come to know? More importantly, how can organizations ensure the data these agents rely on is secure, compliant, and well-managed?
Let’s dive in.
AI Agents vs. Generative AI and LLMs — What’s the Difference?
At first glance, AI Agents might seem similar to generative AI tools like ChatGPT or other LLMs. However, there are key distinctions that set them apart:
- Generative AI and LLMs: These tools are designed to generate human-like text, images, or other content based on prompts. They excel at tasks like drafting emails, summarizing documents, or brainstorming ideas. However, they are reactive — they respond to user inputs but don’t act autonomously.
- AI Agents: AI Agents take things a step further. They are proactive, autonomous systems that can perform tasks on behalf of users. Think of them as virtual assistants that can handle complex, multi-step workflows — like reconciling financial statements, managing supply chain logistics, or even generating sales leads — without constant human intervention.
The key difference lies in their ability to act autonomously and leverage external systems — like data stores — to extend their knowledge and capabilities.
Why This Matters
As AI Agents become more integrated into business processes, they will rely heavily on external data sources to perform their tasks effectively. This introduces new opportunities for efficiency but also new risks — particularly around data security, privacy, and compliance.
How AI Agents Work: The Role of Data Stores and Retrieval Augmented Generation (RAG)
Like all good assistants, AI Agents rely heavily on external data sources to perform their tasks effectively. This is where data stores and Retrieval Augmented Generation (RAG) come into play.
- Data Stores: Are the knowledge backbone of AI Agents— it’s how they extend their knowledge by connecting to external systems, such as data stores. These data stores are typically implemented as bases de datos vectoriales, which allow agents to access and process vast amounts of structured and unstructured data, including:
- Website Content
- Datos estructurados (e.g., CSV files, spreadsheets)
- Datos no estructurados (e.g., PDFs, emails, text documents)
However, this reliance on external data introduces risks, particularly around data leakage and security vulnerabilities. If not properly managed, sensitive information stored in these data stores could be exposed, leading to compliance issues and reputational damage.
- Retrieval Augmented Generation (RAG): RAG applications enable AI Agents to go beyond their foundational training data by retrieving relevant information from external sources in real-time. This allows agents to provide more accurate, context-aware responses and take informed actions.
For example, an AI Agent tasked with customer support can pull product details from a company’s database to answer specific queries, or a financial agent can access transaction records to reconcile accounts.
The Challenge of Data Security in RAG
While RAG enhances the capabilities of AI Agents, it also increases the attack surface for data breaches. Organizations must ensure that the accessed data is secure, compliant, and properly governed.
The Risks of AI Agents: Data Leakage and Security Concerns
While AI Agents offer immense potential, they also introduce new risks, particularly around data security and compliance. Some of those risks include:
- Access to Sensitive Data: AI Agents often require access to sensitive business data to perform their tasks. If this data is not properly secured, it could be exposed to usuarios no autorizados.
- Vector Database Vulnerabilities: Data stores, often implemented as vector databases, can become targets for cyberattacks if not adequately protected.
- Retos de cumplimiento: Organizations must ensure that their AI Agents comply with data privacy regulations like GDPR, CCPA, and others. Failure to do so can result in hefty fines and legal repercussions.
Why Traditional Security Measures Fall Short
Traditional data security solutions are not designed to handle the unique challenges posed by AI ecosystems. Organizations need specialized tools to discover, classify, and secure AI-related data assets, including vector databases and models.
Secure Your AI Ecosystem with BigID Next
AI Agents are shifting the way work gets done. From automating routine tasks to driving complex business processes, these intelligent systems have the potential to transform industries and unlock new levels of productivity. However, with great power comes great responsibility. Organizations must ensure that the data fueling their AI Agents is secure, compliant, and well-governed.
BigID Siguiente Es la primera plataforma de datos modular que aborda la totalidad del riesgo de datos en seguridad, cumplimiento normativo e IA. Elimina la necesidad de soluciones dispares y aisladas al combinar las capacidades de DSPM, DLP, data access governance, AI model governance, privacy, retención de datos, and more — all within a single, cloud-native platform.
Así es como BigID Next ayuda a las organizaciones a transformar el riesgo de la IA:
- Descubrimiento automático completo de activos de datos de IA: El autodescubrimiento de BigID Next va más allá del escaneo de datos tradicional, ya que detecta activos de IA, tanto administrados como no administrados, en entornos locales y en la nube. BigID Next identifica, inventaría y mapeaba automáticamente todos los activos de datos relacionados con la IA, incluyendo modelos, conjuntos de datos y vectores.
- Primer DSPM en escanear bases de datos de vectores de IA: Durante el proceso de Recuperación-Generación Aumentada (RAG), los vectores conservan rastros de los datos originales a los que hacen referencia, que pueden incluir información confidencial inadvertidamente. BigID Next identifica y mitiga la exposición de... Información de identificación personal (PII) y otros datos de alto riesgo integrados en vectores, lo que garantiza que su flujo de trabajo de IA se mantenga seguro y conforme.
- Asistentes de IA para la seguridad, la privacidad y el cumplimiento: BigID Next presenta los primeros asistentes de IA de su tipo, diseñados para ayudar a las empresas a priorizar los riesgos de seguridad, automatizar los programas de privacidad y apoyar a los administradores de datos con recomendaciones inteligentes. Estos asistentes basados en IA garantizan que el cumplimiento sea proactivo, no reactivo.
- Alerta y gestión de posiciones de riesgo: Los sistemas de IA presentan riesgos para los datos que van más allá de los propios datos y se extienden a quienes tienen acceso a datos y modelos sensibles. Las alertas mejoradas de postura de riesgo de BigID Next rastrean y gestionan continuamente los riesgos de acceso, proporcionando visibilidad sobre quién puede acceder a qué datos. Esto es especialmente crucial en entornos de IA, donde grandes grupos de usuarios suelen interactuar con modelos y conjuntos de datos sensibles. Con BigID Next, puede evaluar proactivamente la exposición de los datos, implementar controles de acceso y reforzar la seguridad para proteger sus datos de IA.
Para ver como BigID Siguiente can help you confidently embrace the power of AI Agents — consiga hoy mismo una demostración 1:1 con nuestros expertos.
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