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The rapid adoption of generative AI is transforming Swiss companies’ internal processes, boosting team efficiency and deliverable quality. However, this innovation does not intrinsically guarantee security: integrating language models into your development pipelines or business tools can open exploitable gaps for sophisticated attackers. Faced with threats such as malicious prompt injection, deepfake creation, or hijacking of autonomous agents, a proactive cybersecurity strategy has become indispensable. IT leadership must now embed rigorous controls from the design phase through the deployment of GenAI solutions to protect critical data and infrastructure.
Assessing the Risks of Generative Artificial Intelligence Before Integration
Open-source and proprietary language models can contain exploitable vulnerabilities as soon as they go into production without proper testing. Without in-depth evaluation, malicious prompt injection or authentication bypass mechanisms become entry points for attackers.
Code Injection Risks
LLMs expose a new attack surface: code injection. By carefully crafting prompts or exploiting flaws in API wrappers, an attacker can trigger unauthorized command execution or abuse system processes. Continuous Integration (CI) and Continuous Deployment (CD) environments become vulnerable if prompts are not validated or filtered before execution.
In certain configurations, malicious scripts injected via a model can automatically propagate to various test or production environments. This stealthy spread compromises the entire chain and can lead to sensitive data exfiltration or privilege escalation. Such scenarios demonstrate that GenAI offers no native security.
To mitigate these risks, organizations should implement prompt-filtering and validation gateways. Sandboxing mechanisms for training and runtime environments are also essential to isolate and control interactions between generative AI and the information system.
Deepfakes and Identity Theft
Deepfakes generated by AI services can damage reputation and trust. In minutes, a falsified document, voice message, or image with alarming realism can be produced. For a company, this means a high risk of internal or external fraud, blackmail, or disinformation campaigns targeting executives.
Authentication processes based solely on visual or voice recognition without cross-verification become obsolete. For example, an attacker can create a voice clone of a senior executive to authorize a financial transaction or amend a contract. Although deepfake detection systems have made progress, they require constant enrichment of reference datasets to remain effective.
It is crucial to strengthen controls with multimodal biometrics, combine behavioral analysis of users, and maintain a reliable chain of traceability for every AI interaction. Only a multilayered approach will ensure true resilience against deepfakes.
Authentication Bypass
Integrating GenAI into enterprise help portals or chatbots can introduce risky login shortcuts. If session or token mechanisms are not robust, a well-crafted prompt can reset or forge access credentials. When AI is invoked within sensitive workflows, it can bypass authentication steps if these are partially automated.
In one observed incident, an internal chatbot linking knowledge bases and HR systems allowed retrieval of employee data without strong authentication, simply by exploiting response-generation logic. Attackers used this vulnerability to exfiltrate address lists and plan spear-phishing campaigns.
To address these risks, strengthen authentication with MFA, segment sensitive information flows, and limit generation and modification capabilities of unsupervised AI agents. Regular log reviews also help detect access anomalies.
The Software Supply Chain Is Weakened by Generative AI
Dependencies on third-party models, open-source libraries, and external APIs can introduce critical flaws into your architectures. Without continuous auditing and control, integrated AI components become attack vectors and compromise your IT resilience.
Third-Party Model Dependencies
Many companies import generic or specialized models without evaluating versions, sources, or update mechanisms. Flaws in an unpatched open-source model can be exploited to insert backdoors into your generation pipeline. When these models are shared across multiple projects, the risk of propagation is maximal.
Poor management of open-source licenses and versions can also expose the organization to known vulnerabilities for months. Attackers systematically hunt for vulnerable dependencies to trigger data exfiltration or supply-chain attacks.
Implementing a granular inventory of AI models, coupled with an automated process for verifying updates and security patches, is essential to prevent these high-risk scenarios.
API Vulnerabilities
GenAI service APIs, whether internal or provided by third parties, often expose misconfigured entry points. An unfiltered parameter or an unrestricted method can grant access to debug or administrative functions not intended for end users. Increased bandwidth and asynchronous calls make anomaly detection more complex.
In one case, an automatic translation API enhanced by an LLM allowed direct queries to internal databases simply by chaining two endpoints. This flaw was exploited to extract entire customer data tables before being discovered.
Auditing all endpoints, enforcing strict rights segmentation, and deploying intelligent WAFs capable of analyzing GenAI requests are effective measures to harden these interfaces.
Code Review and AI Audits
The complexity of language models and data pipelines demands rigorous governance. Without a specialized AI code review process—including static and dynamic analysis of artifacts—it is impossible to guarantee the absence of hidden vulnerabilities. Traditional unit tests do not cover the emergent behaviors of generative agents.
For example, a Basel-based logistics company discovered, after an external audit, that a fine-tuning script contained an obsolete import exposing an ML pod to malicious data corruption. This incident caused hours of service disruption and an urgent red-team campaign.
Establishing regular audit cycles combined with targeted attack simulations helps detect and remediate these flaws before they can be exploited in production.
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AI Agents Expand the Attack Surface: Mastering Identities and Isolation
Autonomous agents capable of interacting directly with your systems and APIs multiply intrusion vectors. Without distinct technical identities and strict isolation, these agents can become invisible backdoors.
Technical Identities and Permissions
Every deployed AI agent must have a unique technical identity and a clearly defined scope of permissions. In an environment without MFA or short-lived tokens, a single compromised API key can grant an agent full access to your cloud resources.
A logistics service provider in French-speaking Switzerland, for instance, saw an agent schedule automated file transfers to external storage simply because an overly permissive policy allowed writes to an unrestricted bucket. This incident revealed a lack of role separation and access quotas for AI entities.
To prevent such abuses, enforce the principle of least privilege, limit token lifespans, and rotate access keys regularly.
Isolation and Micro-Segmentation
Network segmentation and dedicated security zones for AI interactions are essential. An agent should not communicate freely with all your databases or internal systems. Micro-segmentation limits lateral movement and rapidly contains potential compromises.
Without proper isolation, an agent compromise can spread across microservices, particularly in micro-frontend or micro-backend architectures. Staging and production environments must also be strictly isolated to prevent cross-environment leaks.
Implementing application firewalls per micro-segment and adopting zero-trust traffic policies serve as effective safeguards.
Logging and Traceability
Every action initiated by an AI agent must be timestamped, attributed, and stored in immutable logs. Without a SIEM adapted to AI-generated flows, logs may be drowned in volume and alerts can go unnoticed. Correlating human activities with automated actions is crucial for incident investigations.
In a “living-off-the-land” attack, the adversary uses built-in tools provided to agents. Without fine-grained traceability, distinguishing legitimate operations from malicious ones becomes nearly impossible. AI-enhanced behavioral monitoring solutions can detect anomalies before they escalate.
Finally, archiving logs offline guarantees their integrity and facilitates post-incident analysis and compliance audits.
Integrating GenAI Security into Your Architecture and Governance
An AI security strategy must cover both technical design and governance, from PoC through production.
Combining modular architecture best practices with AI red-teaming frameworks strengthens your IT resilience against emerging threats.
Implementing AI Security Best Practices
At the software-architecture level, each generation module should be encapsulated in a dedicated service with strict ingress and egress controls. Encryption libraries, prompt-filtering, and token management components must reside in a cross-cutting layer to standardize security processes.
Using immutable containers and serverless functions reduces the attack surface and simplifies updates. CI/CD pipelines should include prompt fuzzing tests and vulnerability scans tailored to AI models. See our guide on CI/CD pipelines for accelerating deliveries without compromising quality, and explore hexagonal architecture and microservices for scalable, secure software.
Governance Framework and AI Red Teaming
Beyond technical measures, establishing an AI governance framework is critical. Define clear roles and responsibilities, model validation processes, and incident-management policies tailored to generative AI.
Red-teaming exercises that simulate targeted attacks on your GenAI workflows uncover potential failure points. These simulations should cover malicious prompt injection, abuse of autonomous agents, and data-pipeline corruption.
Finally, a governance committee including the CIO, CISO, and business stakeholders ensures a shared vision and continuous AI risk management.
Rights Management and Model Validation
The AI model lifecycle must be governed: from selecting fine-tuning datasets to production deployment, each phase requires security reviews. Access rights to training and testing environments should be restricted to essential personnel.
An internal model registry—with metadata, performance metrics, and audit results—enables version traceability and rapid incident response. Define decommissioning and replacement processes to avoid prolonged service disruptions.
By combining these practices, you significantly reduce risk and build confidence in your GenAI deployments.
Secure Your Generative AI with a Proactive Strategy
Confronting the new risks of generative AI requires a holistic approach that blends audits, modular architecture, and agile governance for effective protection. We’ve covered the importance of risk assessment before integration, AI supply-chain control, agent isolation, and governance structure.
Each organization must adapt these principles to its context, leveraging secure, scalable solutions. Edana’s experts are ready to collaborate on a tailored, secure roadmap—from PoC to production.
