Risks of Unsupervised LLM Implementation

AI agents are not yet safe for unsupervised use in enterprise environments The German Federal Office for Information Security (BSI) and France’s ANSSI have just released updated guidance on the secure integration of Large Language Models (LLMs). Their key message? Fully autonomous AI systems without human oversight are a security risk and should be avoided. As LLMs evolve into agentic systems capable of autonomous decision-making, the risks grow exponentially. From Prompt Injection attacks to unauthorized data access, the threats are real and increasingly sophisticated. The updated framework introduces Zero Trust principles tailored for LLMs: 1) No implicit trust: every interaction must be verified. 2) Strict authentication & least privilege access – even internal components must earn their permissions. 3) Continuous monitoring – not just outputs, but inputs must be validated and sanitized. 4) Sandboxing & session isolation – to prevent cross-session data leaks and persistent attacks. 5) Human-in-the-loop, i.e., critical decisions must remain under human control. Whether you're deploying chatbots, AI agents, or multimodal LLMs, this guidance is a must-read. It’s not just about compliance but about building trustworthy AI that respects privacy, integrity, and security. Bottom line: AI agents are not yet safe for unsupervised use in enterprise environments. If you're working with LLMs, it's time to rethink your architecture.

A new “Real Harm” dataset of publicly documented LLM failures, and paper, has been published by Giskard, and although reading it feels like rubbernecking, if you want examples of what can go wrong, slow the roll and urge more caution in your company, it has powerful utility. The taxonomy is broken into: - Misinformation & Fabrication - Interaction Disconnect - Operational Disruption - Brand-Damaging Conduct - Criminal Conduct - Violence & Toxicity - Bias & Discrimination - Privacy Violation - Unsettling Interaction - Vulnerable Individual Misguidance The authors say they will keep updating with new cases, and hopefully they will, and you can contribute cases to Github and their website. The “Vulnerable Individual Misguidance” is particularly topical with the revelation the number one use case for LLMs globally is “Companionship and Therapy”. I read a research paper recently that showed that emotionally engaging with AI can worsen pre-existing mental health problems, especially for those suffering with depression, delusion, or psychosis. We’re in uncharted territory and need far more caution. I agree with the author’s conclusions that current guardrails are insufficient as standalone solutions, and the case they make for LLMs checking other LLMs should be part of the solution, as they are more adept at handling contextual, domain-specific, and nuanced risk categories. The harms LLMs can cause to deployers and users can be real and costly, and we need to face this head-on.

The EDPB recently published a report on AI Privacy Risks and Mitigations in LLMs.   This is one of the most practical and detailed resources I've seen from the EDPB, with extensive guidance for developers and deployers. The report walks through privacy risks associated with LLMs across the AI lifecycle, from data collection and training to deployment and retirement, and offers practical tips for identifying, measuring, and mitigating risks.   Here's a quick summary of some of the key mitigations mentioned in the report:   For providers: • Fine-tune LLMs on curated, high-quality datasets and limit the scope of model outputs to relevant and up-to-date information. • Use robust anonymisation techniques and automated tools to detect and remove personal data from training data. • Apply input filters and user warnings during deployment to discourage users from entering personal data, as well as automated detection methods to flag or anonymise sensitive input data before it is processed. • Clearly inform users about how their data will be processed through privacy policies, instructions, warning or disclaimers in the user interface. • Encrypt user inputs and outputs during transmission and storage to protect data from unauthorized access. • Protect against prompt injection and jailbreaking by validating inputs, monitoring LLMs for abnormal input behaviour, and limiting the amount of text a user can input. • Apply content filtering and human review processes to flag sensitive or inappropriate outputs. • Limit data logging and provide configurable options to deployers regarding log retention. • Offer easy-to-use opt-in/opt-out options for users whose feedback data might be used for retraining.   For deployers: • Enforce strong authentication to restrict access to the input interface and protect session data. • Mitigate adversarial attacks by adding a layer for input sanitization and filtering, monitoring and logging user queries to detect unusual patterns. • Work with providers to ensure they do not retain or misuse sensitive input data. • Guide users to avoid sharing unnecessary personal data through clear instructions, training and warnings. • Educate employees and end users on proper usage, including the appropriate use of outputs and phishing techniques that could trick individuals into revealing sensitive information. • Ensure employees and end users avoid overreliance on LLMs for critical or high-stakes decisions without verification, and ensure outputs are reviewed by humans before implementation or dissemination. • Securely store outputs and restrict access to authorised personnel and systems.   This is a rare example where the EDPB strikes a good balance between practical safeguards and legal expectations. Link to the report included in the comments.   #AIprivacy #LLMs #dataprotection #AIgovernance #EDPB #privacybydesign #GDPR

Most LLM strategies are castles built on sand. Everyone wants the benefits of AI—but most ignore the structural risks. Hype races ahead of reality, and somewhere between "ship it" and "scale it," security and governance get dropped. The OWASP LLM Cybersecurity & Governance Checklist is a wake-up call. Somehow, I missed this gem last year… It outlines what too many AI deployments lack: -No clear threat models for GenAI applications. -Poor asset inventories—do you even know what models you're using and where? -Inconsistent AI training on security and privacy. -Governance frameworks that start after the breach, not before the build. Worse, many leaders still treat LLMs like regular software when the risks are categorically different. Hallucinations, nondeterminism, emergent threats—they're not bugs, they’re the design. Here’s what I see in the field: Red teams exploit prompt injections, bypass filters, and leverage LLMs as attack surfaces—not just tools. The checklist is not comprehensive. But it's a necessary starting point. Are your teams evaluating AI risk like it’s 2025—or still stuck in 2021? #ai #cybersecurity #governance #llmsecurity #mlsecops #devsecops #ciso #riskmanagement

The ugly truth about LLMs in healthcare It's not all groundbreaking innovations and improved patient care - LLMs come with serious ethical concerns that could put lives at risk. Most companies are not understanding the risks of LLMs in healthcare. Here are five mistakes to avoid: Ignoring ethical implications. → Rapid adoption without guidelines puts patient safety at risk. Instead, do this ↳ Implement robust ethical frameworks before deployment ↳ Engage in ongoing ethical review processes Overlooking bias issues. → LLMs can perpetuate harmful gender, cultural, or racial biases. Instead, do this ↳ Regularly audit LLMs for bias in healthcare contexts ↳ Diversify training data to minimize biased outputs Neglecting human oversight. → Overreliance on LLMs can lead to potentially life-threatening errors. Instead, do this ↳ Establish clear protocols for human verification of LLM outputs ↳ Train healthcare professionals on LLM limitations and proper use Disregarding privacy concerns. → LLMs may compromise sensitive patient data if not properly managed. Instead, do this ↳ Implement stringent data protection measures ↳ Ensure compliance with healthcare privacy regulations Underestimating misinformation risks. → LLMs can produce convincing but inaccurate medical content. Instead, do this ↳ Develop fact-checking mechanisms for LLM-generated information ↳ Educate patients on the limitations of AI-generated medical advice LLMs are powerful tools, but they require careful implementation in healthcare. I hope to touch upon some of these issues today at a panel discussion at the WHX Tech. What are you doing to implement LLMs ethically in your organization? ________ Read more about the topic here: https://lnkd.in/eK2ceNJN