Certified: The ISACA AAISM Audio Course

In this episode, we’re going to learn how to think about the A I threat landscape in a way that is realistic, useful, and not driven by hype. New learners often hear dramatic stories about A I, and that can make threats feel either overwhelming or imaginary, depending on how the story is told. The purpose of a threat landscape is not to scare you; it is to help you anticipate what kinds of misuse and attack patterns are likely, so you can design safer systems and recognize early warning signs. With A I, threats can involve classic cybersecurity goals like stealing data or disrupting services, but they can also involve manipulating model behavior, abusing integrations, and causing harmful outputs that damage trust. The best way to understand this landscape is through abuse cases, which are realistic stories of how someone could misuse the system and what harm could result. By the end, you should be able to describe common A I abuse cases, why they work, and how to reason about them without becoming tool obsessed or paranoid.

Before we continue, a quick note: this audio course is a companion to our course companion books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.

An abuse case is a description of how a system could be used in a harmful or unintended way, either by accident or on purpose. Abuse cases are different from theoretical vulnerabilities because they focus on real outcomes, like data exposure, unsafe decisions, or service disruption. They also help you stay grounded, because they start from how the system is actually used, not from how you wish it were used. For beginners, abuse cases are a bridge between technical details and risk decisions, because you can ask simple questions like who would try this, what would they gain, and what would the harm be. Abuse cases are also powerful because they highlight where controls should be placed, such as at the input, at the data access boundary, or at the output delivery channel. When you build a threat landscape using abuse cases, you create a map of likely dangers rather than a pile of scary words. That map helps you prioritize your defenses.

A useful way to organize A I abuse cases is to start with what the attacker or misuser wants, because motives shape methods. Some actors want data, such as customer records, internal documents, credentials, or intellectual property. Some actors want influence, meaning they want the system to produce outputs that steer users toward harmful actions or wrong decisions. Some actors want disruption, such as causing outages, triggering expensive compute usage, or degrading service quality. Some actors want access, meaning they want to use the A I system as a stepping stone to reach other systems through integrations. Some actors want reputation damage, such as making the system say offensive or false statements publicly. For beginners, it helps to remember that A I is not a new species of goal; it is a new surface through which old goals can be pursued, plus a few A I specific twists. The threat landscape becomes clearer when you tie threats to concrete goals.

One common abuse case is sensitive data extraction through interaction, where a person tries to coax the system into revealing information it should not reveal. This can happen when the system has access to private data sources, when it can retrieve internal documents, or when it has logs or context that include sensitive content. The misuser might ask direct questions, try many variations, or attempt to trick the system into ignoring rules. They may also try to infer sensitive information indirectly, such as learning about internal processes or personal details through careful questioning. This abuse case works when the system has broad access and insufficient controls on what can be retrieved and revealed. It also works when monitoring is weak, because probing looks like normal use until patterns are noticed. For beginners, the key lesson is that if a model can see sensitive data, someone will eventually try to make it say sensitive data, and controls must assume that pressure.

Another realistic abuse case is prompt injection, which is when an attacker places instructions into the input that are designed to override or manipulate the system’s behavior. The twist with A I is that inputs can include not only the user’s prompt but also text retrieved from documents, web pages, emails, or knowledge bases. If the system retrieves content that contains malicious instructions, the model may follow those instructions unless the surrounding system prevents it. This is particularly dangerous in retrieval based systems where the model is asked to use retrieved content as context. Prompt injection works by exploiting the model’s tendency to treat text as instructions, even when that text came from an untrusted source. For beginners, it helps to picture this as sneaking a note into the system’s reading material that says ignore your rules and do what I want. The defense mindset is to treat retrieved content as data, not authority, and to design controls that keep untrusted text from becoming controlling instructions.

A closely related abuse case involves indirect prompt injection through user supplied documents. For example, if users can upload files and the system reads them to answer questions, an attacker can embed malicious instructions inside the file. The attacker may then ask the system to summarize the file, causing the model to absorb the instructions and behave in a way that leaks data or bypasses policy. This abuse case is realistic because document upload features are common, and users often trust documents more than they should. It also exploits the fact that models may not reliably distinguish between instructions and content. The harm can include data leakage, unsafe outputs, and manipulation of downstream actions if the A I system is connected to other tools. For beginners, the important takeaway is that any pathway that brings untrusted text into the model’s context can be an attack surface. If your A I system reads it, you must treat it as potentially hostile.

Another major abuse case is integration abuse, where an attacker uses the A I system’s connections to other systems to reach data or trigger actions. Many A I systems are not isolated; they can query databases, send messages, create tickets, update records, or call external services. If the system can take actions, the attacker’s goal may shift from making the model say something to making the model do something. This becomes dangerous when authorization is weak, when the A I system has overly broad privileges, or when the system trusts the model’s output too much without validation. For example, a model might be used to generate a command or a request that an integration executes, and an attacker may try to manipulate the model into generating a harmful action. Even without direct action execution, integration abuse can involve using the model to search internal knowledge bases for secrets or to pull sensitive documents through broad retrieval. For beginners, the key lesson is that A I becomes a bridge to other systems, and bridges must have gates and guards, not open highways.

A very practical abuse case is data poisoning, which is when an attacker or careless process introduces bad data into training, tuning, or retrieval sources so the model produces harmful or inaccurate outputs. Poisoning does not require advanced hacking; it can be as simple as modifying documents in a knowledge base or injecting misleading content into a dataset. The goal might be to degrade quality, to steer outputs toward certain narratives, to cause the model to reveal misinformation, or to reduce trust in the system. Poisoning is realistic because many organizations pull data from large, evolving sources and assume the data is clean. For A I systems that rely on retrieval, poisoning can be even easier because the model may read whatever is in the knowledge base today, not what was there when the system was designed. For beginners, the lesson is that data is part of the attack surface, and protecting data integrity is a security requirement, not a data science preference. If the knowledge source can be changed by the wrong person, it can become a weapon.

Model theft and intellectual property extraction is another abuse case that matters when an organization invests in custom models or proprietary prompts and workflows. Attackers may attempt to replicate model behavior by making many queries and using the outputs to train a substitute model, which can allow them to steal value without direct access to the model weights. They may also try to extract proprietary system prompts, policy rules, or internal instructions by probing the model and searching for leakage. This abuse case is realistic when the model is exposed publicly or widely internally without strong controls, and when monitoring does not detect unusual high volume query patterns. The harm includes loss of competitive advantage and increased risk if an attacker learns how your controls work. For beginners, it helps to see this as reverse engineering through conversation, where the attacker asks enough questions to copy what makes the system special. The defense mindset includes rate awareness, access control, and monitoring for unusual patterns that suggest systematic extraction attempts.

Denial of service and resource exhaustion is a classic threat that also applies to A I, sometimes in amplified form because A I workloads can be expensive. An attacker might send a flood of requests, craft prompts that consume excessive computation, or trigger repeated retries by exploiting error conditions. Even without malicious intent, misuse can cause resource exhaustion when many users send heavy requests or upload large content without constraints. The harm includes slow response times, outages, increased costs, and degraded service for legitimate users. This abuse case is realistic because it does not require bypassing complex controls; it simply leverages scale and cost. For beginners, the key idea is that availability is a security property, and A I systems must protect availability through sensible limits, prioritization, and monitoring. If a system cannot stay available under stress, it becomes unreliable, and users will work around it in ways that create new risks.

Another abuse case that deserves attention is harmful content generation and social manipulation, especially for A I systems that interact with customers or influence decisions. Attackers may try to make the system produce offensive language, targeted harassment, unsafe instructions, or persuasive misinformation. In some contexts, the goal is not to harm the user directly but to create screenshots that damage reputation and trust. This is realistic because A I outputs are easily shareable and can be taken out of context, and because public perception of A I safety is sensitive. The risk increases when the system is customer facing, when outputs are posted publicly, or when the system’s tone and authority cause users to trust it too much. For beginners, the lesson is that safety and security overlap, because harmful outputs can be a form of impact even when no data is stolen. Controls here involve policy enforcement, monitoring, and thoughtful product design that limits how outputs are used in high stakes contexts.

As you build a threat landscape, it is important to remain realistic by considering who the likely actors are and what access they have. External attackers may have no credentials and may only interact through public interfaces, which shapes the abuse cases that are feasible. Insiders may have legitimate access and may be able to change data sources or configurations, which creates different threats like poisoning and privilege misuse. Compromised accounts sit in the middle, because they appear legitimate but act maliciously. Vendors and third parties can also be part of the threat landscape, not necessarily as villains, but as dependency risks that can introduce exposure if their systems are compromised or if their controls are weaker than yours. Beginners should see that the threat landscape is not one villain; it is a set of realistic actor types with different capabilities. When you match abuse cases to actor capabilities, you avoid wasting energy on improbable fantasies and focus on likely paths.

The final step is connecting abuse cases to practical detection and prevention ideas, because a threat landscape is only useful if it leads to action. For example, data extraction and prompt injection risks point to controls around data access boundaries, content handling, and monitoring for probing patterns. Integration abuse points to least privilege, validation of actions, and safe design where the model cannot directly execute high impact actions without checks. Poisoning points to data integrity controls and change monitoring on knowledge sources. Model theft points to access control, rate monitoring, and detection of systematic query patterns. Denial of service points to resource limits and availability monitoring. Harmful content points to policy enforcement and careful use of A I in customer facing contexts. The purpose is not to memorize a list, but to learn the habit of asking what could be abused here and what control would reduce that abuse. When you practice that habit, you become better at designing safer systems and responding faster when signals appear.

As we close, identifying the A I threat landscape using realistic abuse cases means grounding your understanding in how real actors could cause real harm through the systems you actually build and operate. Abuse cases like sensitive data extraction, prompt injection, integration abuse, data poisoning, model theft, resource exhaustion, and harmful content generation cover many of the most common risk stories. These stories are useful because they connect threats to system touchpoints, such as inputs, retrieval, data access, outputs, and downstream actions. A realistic threat landscape also considers different actors, like external attackers, insiders, and compromised accounts, because their access shapes what they can do. When you understand these abuse cases, you can choose better controls, monitor more intelligently, and make risk decisions that protect trust while still allowing A I to deliver real business value. That is exactly what Domain 2 is aiming for: practical threat awareness that leads to safer opportunity, not fear.