What are the main data security risks in Generative AI according to OWASP AI Security guidance?
OWASP AI Security guidance identifies four primary data risks that organizations face when deploying Generative AI. Specifically, these risks go beyond traditional data protection concerns because AI systems introduce new attack surfaces through training data, model outputs, and agent behaviors. Moreover, many of these risks emerge from how employees interact with AI tools informally, outside of sanctioned platforms.
In practice, the four main risks are:
- Sensitive Data Leakage: models output verbatim sensitive strings such as personal data, API keys, or internal secrets embedded in training data
- Agent Identity and Credential Exposure: over-provisioned tokens and service accounts allow compromised agents to access far more data than their task requires
- Shadow AI: employees input sensitive organizational data into unsanctioned public AI tools, creating uncontrolled data exposure outside corporate security boundaries
- Data and Model Poisoning: compromised supply chains and corrupted training datasets introduce backdoors or biases that persist into production
What is Prompt Injection and how does it threaten AI systems?
Prompt Injection, classified as LLM01 in the OWASP Top 10 for LLM Applications, is one of the most widespread attack vectors against Large Language Models (LLMs). Specifically, it exploits the AI system’s inability to reliably separate legitimate developer instructions from untrusted user input. As a result, attackers can override safety constraints, exfiltrate data, or trigger unauthorized actions, all through crafted text inputs.
In practice, attacks take two forms. Direct prompt injections override core safety constraints through the user interface. However, indirect prompt injections are often more dangerous. Specifically, attackers embed malicious instructions inside external content that the AI naturally ingests, such as parsed webpages, PDFs, or YouTube transcripts. As a result, a user browsing a compromised webpage through an AI assistant can unknowingly trigger the AI to leak system instructions or perform actions on the attacker’s behalf.
How can organizations secure Agentic AI and multi-agent systems?
Agentic AI systems introduce risks that traditional application security controls do not address. Specifically, vulnerabilities like tool misuse, excessive agency, and goal manipulation can cause AI agents to take harmful autonomous actions. Therefore, organizations must embed security controls throughout the development lifecycle rather than adding them after deployment.
In practice, four controls define a secure agentic architecture:
- Strict sandboxing: isolate agent execution environments so a compromised agent cannot reach systems outside its defined scope
- Just-in-Time (JIT) access with ephemeral credentials: limit the window of potential misuse by granting tool invocation rights only for the duration of the specific task
- Control plane and data plane separation: in multi-agent architectures, prevent a single compromised agent from disrupting or manipulating the entire system
- Human-in-the-Loop (HITL) approval workflows: require mandatory human review before any high-impact operation executes
How should organizations define and prepare for AI security incidents?
An AI security incident is an event where the development, use, or malfunction of an AI system leads to unintended, harmful, or risk-elevating outcomes. Moreover, AI incidents differ fundamentally from traditional cyber incidents. Specifically, they involve stochastic (probabilistic) generation, prompt-driven manipulation, and complex black-box algorithms that standard incident response playbooks do not cover. As a result, organizations need dedicated preparation before an incident occurs.
In practice, two steps are essential. First, include Generative AI risks as a formal, distinct category within your enterprise risk register, rather than treating them as a subset of existing software or data risks. Second, establish a specialized AI Incident Response Team (AIRT) that includes machine learning engineers and AI security specialists. In fact, diagnosing issues like model drift, adversarial inputs, and poisoned training data requires skills that traditional security operations center (SOC) analysts typically do not have.
What criteria should organizations use when evaluating AI Red Teaming vendors?
AI Red Teaming requires fundamentally different skills from traditional penetration testing. Specifically, vendors must understand how Generative AI systems fail under adversarial conditions, not just how networks and applications do. Therefore, organizations should evaluate vendors carefully before engaging them for AI security assessments.
In practice, strong vendors demonstrate three capabilities. First, they construct novel, multi-step adversarial workflows tailored to the organization’s specific data, models, and use cases. Second, they show deep technical understanding of advanced GenAI attack surfaces, including Model Context Protocol (MCP) privilege escalation, unsafe tool-calling semantics, and inter-agent contamination. Third, they provide reproducible, measurable findings rather than anecdotal results.
Conversely, organizations should watch for three red flags:
- Vendors passing off static, public jailbreak libraries as genuine red teaming
- Failure to provide reproducible metrics that demonstrate coverage and severity
- Ignoring systemic tool misuse and focusing only on prompt-level attacks
