Integration of AI systems into critical operational processes requires an immediate shift from reactive defense to proactive risk management to meet the complex threat landscape forming by 2027. As large language models (LLMs) become full-fledged AI agents with access to internal databases, APIs, and ERP systems, the classic security perimeter finally loses its effectiveness. Security executives face a dual challenge: protecting corporate systems from evolving AI attack vectors and ensuring operational resilience amidst a high volume of cyber incidents.
According to the Cisco Cybersecurity Readiness Index 2025, AI fortification is now a fundamental pillar of overall cyber threat readiness. The primary technical challenge is that traditional data loss prevention (DLP) systems and SIEMs do not understand the context of LLM interactions. AI security requires new architectural approaches where control is exercised at the level of semantic analysis and deep data isolation.
Why traditional DLP is powerless against AI agents: anatomy of a semantic leak
Classic DLP systems have been built for years on searching for signatures, regular expressions, and confidentiality labels. However, in the world of generative AI, these methods fail. A semantic data leak occurs when sensitive information leaves the secure perimeter not as a direct file copy, but through a paraphrased or summarized query.
For example, an employee might provide a financial report to a corporate chatbot, asking to replace exact figures with percentage trends. For signature-based DLP, no sensitive patterns were transferred. However, the company's intellectual property has entered the model's context, creating a risk of unauthorized disclosure through prompt injection attacks.
Even more dangerous is the reverse path. When an AI agent, using Retrieval-Augmented Generation (RAG) mechanisms, generates a response, it may access data for which the user lacks permissions. If the model is not restricted by strict access control rules directly in the database, traditional gateways cannot recognize this leak within the mass of generated text.
Threat modeling 2027: integrating AI risks into SOC using MITRE ATT&CK
To effectively counter new attack vectors, organizations must integrate specific AI system threats into the general SOC (Security Operations Center) monitoring perimeter. Mapping SIEM alerts to the MITRE ATT&CK matrix allows for a common language for threat hunting and assessing the real coverage of existing security controls against attacker tactics and techniques.
The relevance of this approach is confirmed by statistics: the ENISA Threat Landscape 2025 report notes that 4,875 incidents were analyzed between July 1, 2024, and June 30, 2025. Essential entities subject to the NIS2 directive accounted for 53.7% of all affected organizations, while digital infrastructure and services accounted for about 27.7% of data leak cases. This underscores the need for strict monitoring of new technology stacks.
When modeling threats for AI systems, the SOC should focus on specific tactics such as jailbreaking, training set compromise, and attempts to recover sensitive training data by analyzing model responses.
Practical implementation of NIST AI RMF 1.0: from management to measurement
The updated NIST CSF 2.0 framework introduced the "Govern" function, which positions cyber risk management as an integral component of corporate governance. This approach allows for a gap analysis of security tools when implementing AI.
For specific AI threats, the NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0) should be used. It is important to remember that this is a voluntary framework, not a mandatory regulatory standard, yet it offers the most comprehensive methodology for protection through four key functions:
- Govern: creating a culture of responsible AI use, security policies, and compliance.
- Map: identifying the context of each model's use, data classification, and attack vectors.
- Measure: evaluating AI models. In critical infrastructure, this must be based on context, security, and reliability, not just response accuracy.
- Manage: implementing technical controls and incident response.
Secure architecture of AI systems: a protected perimeter for LLM integrations
To neutralize semantic leaks, system architecture must be built on the principle of Security by Design. Instead of connecting LLMs directly to corporate databases, semantic DLP gateways must be implemented to analyze the context of queries and responses.
Security begins at the platform level where the software runs. Members of the Intecracy Group technology alliance develop custom solutions and AI agents with built-in security. Specifically, Softengi is certified to the international AI management standard ISO/IEC 42001:2023, confirming a structured approach to AI solution development. The technological foundation for enterprise systems is often the low-code platform UnityBase (a joint development of Intecracy Group companies; InBase is a key, but not the only, developer of the platform). For solutions with high security requirements, the Enterprise or Defence editions of UnityBase are officially recommended.
The UnityBase platform ensures integration security through built-in architectural mechanisms:
- Row-level security (RLS) and Access Control Lists (ACL): if an AI agent is compromised, it technically cannot access data for which the user lacks permissions (rules operate at the kernel level via domain metadata).
- DBMS-agnostic ORM: prevents direct database injections, as queries pass through an abstract metadata layer.
- Full Audit Trail: the platform logs every action of the user and system component, allowing for rapid incident investigation and mapping to MITRE ATT&CK.
Such a foundation allows for the creation of secure enterprise solutions where intelligent information processing occurs exclusively within the organization's controlled perimeter.
| Framework function | Control focus | Practical security measures |
|---|---|---|
| Govern | Organizational policies, defining responsibility for AI risks. | Developing AI usage policies, training staff on threat detection methods, assigning responsible parties. |
| Map | Identifying AI context, data classification, detecting attack vectors. | Creating an AI system registry, classifying data sensitivity, analyzing integration architecture. |
| Measure | Evaluating AI model security and resilience, testing DLP filters. | Conducting regular LLM checks, measuring resistance to manipulation, auditing false positives. |
| Manage | Continuous monitoring in SOC, automatic anomaly blocking. | Integrating AI logs into SIEM, configuring rules for blocking anomalous queries. |
FAQ
How to configure DLP to protect against data leaks via corporate LLMs?
Effective protection requires the implementation of semantic DLP gateways between the user and the model. They analyze the context of queries, blocking attempts to extract sensitive information even in paraphrased form, and rely on strict access policies (RLS/ACL) directly at the platform level.
Which specific threats to AI systems will become most relevant in the coming years?
Organizations should prepare for complex query manipulations (jailbreaking), attempts to compromise training sets (data poisoning), and attack vectors aimed at revealing contextual information. Monitoring these threats should be integrated into the SOC by mapping them to the MITRE ATT&CK matrix.
Is the NIST AI RMF 1.0 framework mandatory for companies under the NIS2 directive?
No, NIST AI RMF 1.0 is a voluntary framework. However, it offers a deeply structured methodology (Govern, Map, Measure, Manage) that helps organizations build proactive protection and meet general regulatory requirements for digital infrastructure risk management.