The generative AI systems present unique security risks that impact both data, operations, and the quality of decisions in an organization. Teams that evaluate these risks often align controls with recognized risk lists and risk profiles, including the OWASP Top 10 for LLM applications and the NIST Generative AI Profile. Many organizations now connect this evaluation work to a generative AI course for managers because managers set access, governance, and approval boundaries.
Common attack paths in GenAI apps
OWASP lists prompt injection among the most significant risks to LLM applications and links it to unauthorised access, information leaks, and impaired decision-making. Attackers make inputs to change model behavior, and commonly attack chat interfaces, document summarizers, and agent-like workflows. Another explanation of OWASP is indirect prompt injection, in which external sources of content, such as web pages or files, contain obfuscated instructions that control model behavior.
OWASP highlights insecure output handling as a separate risk and ties it to downstream exploits, such as code execution, that compromise systems and expose data. Teams create this exposure when downstream tools accept model output as trusted commands, configuration, or executable code. Managers usually influence this risk through tool permissions and workflow design, not through model tuning.
Data leakage and privacy exposure
OWASP classifies sensitive information disclosure as a core risk category for LLM applications, encompassing PII, financial data, health records, credentials, and confidential business data. The same OWASP entry connects weak data protection to unauthorized access, privacy violations, and intellectual property breaches. OWASP also recommends user education and transparency on safe interaction as part of mitigation planning.
NIST considers data privacy risk in terms of leakage and unauthorized use or disclosure of personally identifiable or sensitive information, and it refers to data memorization as a method that exposes training data to attacks. NIST also observed that models can derive sensitive information by integrating data from various sources, even when that information is absent from the prompts. Risk teams usually trace these exposures to real-world risks, such as data minimization, input filtering, and firm boundaries, within the connected data stores.
NIST AI 100-2e2023 describes privacy attacks such as membership inference, data reconstruction, and model extraction as threats that can arise through query access to a model interface. The report also separates attacker objectives into availability breakdown, integrity violations, and privacy compromise for security analysis. Organizations often connect this taxonomy to assurance planning inside a Gen ai course for managers, because managers often decide where public access ends and internal access begins.
Model, supply chain, and lifecycle threats
OWASP highlights supply chain vulnerabilities for LLM applications and connects them to compromised components, services, or datasets that undermine system integrity and trigger breaches or failures. This risk expands beyond software libraries and includes third-party models, fine-tuning adapters, datasets, and hosted plugins. OWASP also emphasizes weak model provenance and limited assurance for published models as practical issues for teams that pull models from public repositories.
OWASP lists training data poisoning as a top risk and ties tampered training data to unsafe responses that can compromise security, accuracy, or ethical behavior. NIST AI 100-2e2023 defines training-time poisoning attacks and explains that an adversary can insert or modify training samples in such attacks. The same NIST report also defines model poisoning as the attacker's control over model parameters and notes its common prevalence in federated learning and supply-chain settings.
NIST AI 600-1 treats information security as a key generative AI risk and states that generative AI both lowers barriers to offensive cyber capabilities and expands the attack surface through issues such as prompt injection and data poisoning. NIST also describes direct prompt injection and indirect prompt injection as attack patterns that can trigger downstream negative consequences in interconnected systems. These links push managers to treat model integration as a security architecture decision, not just a productivity one.
Manager-led controls and training alignment
NIST AI 600-1 positions the Generative AI Profile as a companion resource to the NIST AI Risk Management Framework, and it describes cross-sector use for governing and managing generative AI risks. The profile focuses on governance, content provenance, pre-deployment testing, and incident disclosure as primary considerations for generative AI risk management. A Best Generative Ai course for Managers often mirrors these four areas because managers typically own approval flows and incident escalation paths.
OWASP recommends privilege control and least-privilege access as mitigations for prompt injection, and it also calls for human approval for high-risk actions. Managers operationalise these controls through policy and workflow design, including limits on tool execution, constrained data connectors, and clear boundaries for automation. A generative ai course for managers often covers these controls as practical operating rules for AI assistants inside business systems.
Teams also need a repeatable evaluation method that covers misuse and malfunction risks that NIST lists, including confabulation, information integrity, and information security. Managers influence this method by requiring pre-deployment testing and by enforcing incident disclosure processes that teams can execute consistently. Many generative AI training programs also include governance checklists, risk acceptance records, and role-based access planning, and these topics fit routine manager oversight.
Conclusion
Organizations can evaluate security risks in generative AI systems by focusing on prompt attacks, data leakage, poisoning, supply chain exposure, and operational overreach. Clear governance, least-privilege access, pre-deployment testing, and incident disclosure reduce risk when teams deploy assistants and agent-style workflows. Many Generative Ai training programs present these areas in a structured format, and a Best Generative Ai course for Managers often links them to practical decisions on access, tools, and escalation. A generative ai course for managers supports consistent evaluation when managers apply these controls across teams and systems.