AI innovation continues to impress, and in 2025, we’re looking ahead at the next big thing — AI-Agenten. 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 Generative KI (GenAI) und große Sprachmodelle (LLMs) 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 Vektor-Datenbanken, which allow agents to access and process vast amounts of structured and unstructured data, including:
- Website Content
- Strukturierte Daten (e.g., CSV files, spreadsheets)
- Unstrukturierte Daten (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 unauthorized users.
- Vector Database Vulnerabilities: Data stores, often implemented as vector databases, can become targets for cyberattacks if not adequately protected.
- Compliance-Herausforderungen: 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.
Sichern Sie Ihr KI-Ökosystem mit 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 Weiter ist die erste modulare Datenplattform, die das gesamte Datenrisiko in Bezug auf Sicherheit, Einhaltung gesetzlicher Vorschriften und KI abdeckt. Sie macht unterschiedliche, isolierte Lösungen überflüssig, indem sie die Funktionen von DSPM, DLP, data access governance, AI model governance, privacy, Datenaufbewahrungund mehr – alles innerhalb einer einzigen, Cloud-nativen Plattform.
Here’s how BigID Next helps organizations transform AI risk:
- Vollständige automatische Erkennung von KI-Datenbeständen: Die automatische Erkennung von BigID Next geht über herkömmliches Datenscannen hinaus und erkennt sowohl verwaltete als auch nicht verwaltete KI-Assets in Cloud- und lokalen Umgebungen. BigID Next identifiziert, inventarisiert und ordnet alle KI-bezogenen Datenbestände – einschließlich Modelle, Datensätze und Vektoren – automatisch zu.
- Erster DSPM zum Scannen von KI-Vektordatenbanken: During the Retrieval-Augmented Generation (RAG) process, vectors retain traces of the original data they reference, which can inadvertently include sensitive information. BigID Next identifies and mitigates the exposure of Persönlich identifizierbare Informationen (PII) und andere in Vektoren eingebettete Hochrisikodaten, um sicherzustellen, dass Ihre KI-Pipeline sicher und konform bleibt.
- KI-Assistenten für Sicherheit, Datenschutz und Compliance: BigID Next stellt die ersten agentenbasierten KI-Assistenten ihrer Art vor. Sie unterstützen Unternehmen dabei, Sicherheitsrisiken zu priorisieren, Datenschutzprogramme zu automatisieren und Datenverwalter mit intelligenten Empfehlungen zu unterstützen. Diese KI-gesteuerten Copiloten sorgen dafür, dass Compliance proaktiv und nicht reaktiv bleibt.
- Risikowarnung und -management: AI systems introduce data risks that go beyond the data itself — and extend to those with access to sensitive data and models. BigID Next’s enhanced risk posture alerting continuously tracks and manages access risks, providing visibility into who can access what data. This is especially critical in AI environments, where large groups of users often interact with sensitive models and datasets. With BigID Next, you can proactively assess data exposure, enforce access controls, and strengthen security to protect your AI data.
Um zu sehen, wie BigID Weiter can help you confidently embrace the power of AI Agents — Holen Sie sich noch heute eine 1:1-Demo mit unseren Experten.
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