AI Security: Responsibility or a Business Imperative?

Artificial intelligence has rapidly evolved from an experimental capability into critical business infrastructure. On-premises Large Language Model (LLM) deployments, AI agents, and GenAI applications—used both formally and informally—are now embedded across organizational processes, from customer support and data analytics to software development, security operations, and executive decision-making. In this context, a fundamental question emerges: how prepared are organizations to protect these systems, and is AI Security merely a responsibility—or a true business imperative?

From AI training to AI inference: a paradigm shift with direct security impact

The relationship between AI and cybersecurity is inherently asymmetric. While organizations invest heavily in leveraging AI for threat detection and security automation, the security of their own AI systems is often overlooked. As a result, GenAI models, autonomous agents, and AI-enabled applications are granted access to sensitive data, critical APIs, and core business processes, frequently without security controls aligned to their actual risk and impact.

At the same time, the industry is undergoing a structural shift: the focus is moving from AI training to AI inference. Competitive advantage no longer comes from training models from scratch, but from deploying pre-trained models, integrating them into existing applications, and connecting AI capabilities to critical enterprise data and workflows. This is also the approach delivered by Datanet Systems through its end-to-end AI solution, described in more detail here: “Deploy Enterprise AI applications on your own infrastructure with Datanet Systems.”

This transition effectively turns AI applications into active and exposed attack surfaces, accessible to users and external integrations alike. In this context, AI security becomes an operational and regulatory necessity rather than a “nice to have.” A compromised AI system can lead to flawed decisions, financial losses, and significant compliance breaches—risks further amplified by the rise of Shadow AI, driven by the uncontrolled use of GenAI tools across the organization.

Emerging attack vectors and AI-specific risks

The rapid adoption of AI introduces new risk categories while amplifying existing threat vectors. At the data level, techniques such as prompt injection and data poisoning can manipulate model behavior, resulting in data leakage, controlled exfiltration of sensitive information, or the re-identification of data assumed to be anonymized. From an integrity and trust perspective, adversaries can exploit AI interactions to gain unauthorized access, influence decision-making, or transform AI assistants into enablers of privileged access.

From a governance standpoint, limited visibility into how AI systems process data and generate outputs can lead to non-compliance with regulatory frameworks such as GDPR and the EU AI Act. In parallel, the AI supply chain is emerging as a new risk domain: reliance on external models, libraries, and APIs introduces complex dependencies, where the compromise of a single component can have cascading effects across the entire ecosystem. Taken together, these developments underscore a clear reality: as AI transitions from experimentation to large-scale operational deployment, AI security must be treated as a foundational element of enterprise architecture.

AI Security with Prisma Browser and Prisma AIRS

The market increasingly focuses on AI Security—a comprehensive approach designed to protect artificial intelligence systems against threats that may compromise their confidentiality, integrity, or reliability. AI Security spans the entire AI lifecycle, including data, models, pipelines, infrastructure, and applications. To address these challenges, Palo Alto Networks has developed dedicated solutions for securing AI usage and deployment in enterprise environments, which Datanet Systems, as a certified partner, can integrate into extended and unified security architectures.

Prisma Browser – Securing Web, SaaS, and AI Application Usage Directly in the Browser

As employees increasingly rely on web, SaaS, and GenAI applications for day-to-day activities, securing access to these resources becomes critical. Prisma Browser is a secure enterprise browser designed to provide visibility and control over how users interact with web, SaaS, and GenAI applications—whether accessed from the public internet or from within the organization’s internal infrastructure.

Target Use Cases

Prisma Browser is designed for organizations that:

Use public GenAI applications such as ChatGPT, Microsoft Copilot, Claude, or other browser-accessible AI models.
Access internal AI applications via the browser, including portals, dashboards, and custom in-house applications.
Rely heavily on web and SaaS applications to support critical business processes.
Operate in remote or hybrid work environments, where web access security and control are essential.
Provide access to sensitive applications and data to contractors, partners, or third parties using unmanaged or partially managed endpoints.
Seek to optimize and complement the costs of VDI solutions by delivering a high level of security for web and SaaS access without fully replacing existing environments (as complementary solutions).

Key Capabilities

User- and session-level visibility: Real-time monitoring of how users interact with web, SaaS, and GenAI applications, including the types of data exchanged and the applications in use (e.g., ChatGPT, Copilot), without requiring visibility into the underlying AI models themselves.
In-browser Data Loss Prevention (DLP): Built-in policies that detect and block the transmission of sensitive information to external applications. Prisma Browser can restrict specific data types from being sent to web or GenAI applications; block or control actions such as copy–paste, upload, or download between applications; and dynamically mask sensitive data in real time (e.g., replacing it with placeholders), ensuring it is not actually transmitted.
Contextual control of AI interactions: Prisma Browser enables control over user actions (without altering prompt semantics), preventing sensitive data exfiltration and unauthorized use of information in GenAI interactions.
Isolation of enterprise applications from untrusted endpoints: Prisma Browser protects enterprise applications and data when accessed from unmanaged or partially managed endpoints, reducing exposure to risks such as malware, phishing, or device compromise.
Zero Trust policy enforcement across all applications: Prisma Browser extends context-based Zero Trust policies to all user actions across web, SaaS, and GenAI applications, enforcing identity controls, privileged access, and last-mile data protection. Downloaded files are also encrypted, ensuring data-at-rest protection even outside the organization’s infrastructure.

Business Outcomes

Deploying Prisma Browser delivers tangible benefits:

Reduced risk of sensitive data exposure, including during the use of GenAI applications.
Full visibility and control over how web, SaaS, and GenAI applications are used by employees, contractors, and third-party collaborators.
Advanced activity auditing: in addition to detailed interaction logs, video recordings of user sessions can be triggered when policy violations occur (e.g., DLP breaches).
A reduced attack surface through controlled access and isolation from untrusted endpoints.
Compliance with GDPR, the EU AI Act, and internal security policies—without negatively impacting user productivity.

Prisma AIRS – Runtime Security for AI Applications

As organizations embed AI into mission-critical processes, protecting AI applications, models, and data flows becomes essential. Prisma AI Runtime Security (AIRS) is an advanced solution designed to secure AI applications during runtime, addressing LLM instances, AI agents, and autonomous workflows.

Target Use Cases

Prisma AIRS is designed for:

Custom AI applications developed in-house and deployed on-premises or in the cloud, including containerized AI microservices.
Self-hosted LLMs or LLMs integrated via APIs (open-source or commercial), monitored in real time.
AI agents and autonomous workflows that interact with sensitive data and core business systems.

Key Capabilities

Protection against prompt injection and jailbreak attacks. Detects and blocks attempts to manipulate AI model behavior through malicious prompts or hidden payloads.
Runtime behavioral monitoring. Continuously analyzes model responses and data flows to identify anomalies, abuse, or deviations from expected behavior.
Detection of data leakage and unauthorized usage. Audits access to sensitive data in real time, preventing accidental or intentional data exfiltration.
Granular access control. Enforces fine-grained policies for access to data, models, and APIs, integrated with role-based authentication and authorization mechanisms.
AI governance and auditability. Provides detailed logs, compliance reporting, and integration with SIEM solutions and risk management platforms.

Business Outcomes

Implementing Prisma AIRS delivers measurable benefits:

Increased trust in AI applications deployed in production environments.
Reduced operational and compliance risks, including alignment with GDPR and the EU AI Act.
Comprehensive protection for models, agents, and data, ensuring the integrity and confidentiality of the entire AI ecosystem—from input to output.

Secure End-to-End Private AI with Datanet Systems

For organizations seeking full control over how artificial intelligence is deployed and used, Datanet Systems delivers an enterprise-grade, end-to-end AI solution designed for running AI applications on dedicated infrastructure—on-premises, in private cloud environments, or within hybrid architectures. Delivered as a turnkey solution, it covers the entire technology stack required for AI adoption, including compute infrastructure, storage, networking, orchestration, and integration with existing enterprise applications.

In addition, Datanet Systems ensures comprehensive security for AI applications in line with modern AI Security principles, through the integration of dedicated Palo Alto Networks solutions. With Prisma Browser, the use of web, SaaS, and AI applications is secured at the user level, preventing data leakage and uncontrolled GenAI usage, including Shadow AI scenarios. With Prisma AI Runtime Security (AIRS), AI applications, models, agents, and data flows are protected during runtime against threats such as prompt injection, data leakage, unauthorized access, and model abuse.

Through this integrated approach, Datanet Systems delivers not just a Private AI platform, but a complete and coherent framework for secure, end-to-end AI—covering infrastructure and AI execution, as well as governance, data protection, and runtime security.

As Platinum Partner of Palo Alto Networks in Romania since 2024, Datanet Systems has advanced capabilities to implement and integrate these solutions into the security architectures of both private and public sector organizations. Our consultants can be contacted at sales@datanets.ro.