AI technology, which is now in use by more than two-thirds of organizations, doesn’t just represent an opportunity to streamline and accelerate processes. It also presents a variety of novel security risks.
What’s more, the fundamentally new nature of generative and agentic AI technology means that traditional cybersecurity practices – which were for applications that operated in fully predictable, deterministic ways – don’t suffice for protecting AI. Instead, businesses must develop an AI risk management strategy that extends beyond conventional security controls and risk mitigation.
Read on for guidance as we detail what AI risk management entails, which role it plays in modern cybersecurity, and practical steps for creating and implementing an AI risk management strategy.
What is AI risk management?
AI risk management is the practice of detecting, assessing, and mitigating cybersecurity risks within AI applications, services, and infrastructure. The focus of AI risk management is on reducing the chances of data leakage, security breaches, system misuse, and other potential vulnerabilities associated with AI systems.
AI risk management applies to AI technologies that organizations develop themselves (such as self-built large language models or AI agents), as well as to third-party AI applications or services sourced from external vendors.
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Key types of AI risks
Due to the fast-evolving nature of AI technologies, new risks always have the potential to emerge. Indeed, the list of major AI risks that the typical enterprise faced just a half-decade ago, prior to the emergence of production-ready large language models, was quite different from modern AI risks.
That said, the primary risks that most businesses must address within modern AI systems include the following:
Prompt injection attacks
Prompt injection attacks occur when an attacker embeds malicious instructions into prompts, documents, websites, emails, or other content that an AI system processes, causing the model to ignore its intended instructions and perform unauthorized actions. Prompt injection is especially dangerous when AI systems are connected to external tools, APIs, data sources, or agents because a successful attack can transform an externally exposed AI interface into a pathway for data leakage, unauthorized actions, or broader compromise.
Thus, as organizations rapidly deploy AI-enabled services, security teams must account for the growing number of externally reachable AI entry points that could be exploited through prompt injection.
Training data poisoning and model poisoning
Training data poisoning and model poisoning occur when attackers manipulate the data or artifacts used to train, fine-tune, or update AI models. By introducing malicious or misleading information into the AI development pipeline, attackers can influence model behavior, degrade accuracy, create hidden backdoors, or cause the model to produce unsafe outputs under specific conditions. These risks are particularly challenging because poisoned data or models may be incorporated through complex AI supply chains, third-party sources, or unmanaged development processes, making continuous visibility and validation critical for identifying compromised AI assets before they create exploitable exposure.
Retrieval-augmented generation (RAG) poisoning
RAG poisoning occurs when attackers tamper with the external knowledge sources that AI systems use to retrieve information at inference time (meaning when they respond to user requests, as opposed to when the models are trained). It happens when adversaries inject malicious content into databases, document repositories, websites, or other retrieval sources so that the AI incorporates manipulated information into its responses or decisions. Because RAG systems frequently connect AI models to enterprise data and operational workflows, poisoned retrieval sources can become a conduit for misinformation, prompt injection, unauthorized actions, or data exposure. The risk grows when organizations lack visibility into the external content, integrations, and AI-connected assets that influence model outputs.
AI model extraction and intellectual property theft
AI model extraction attacks enable adversaries to reconstruct, replicate, or approximate a proprietary model by repeatedly querying it and analyzing its outputs. While these attacks may not directly alter model behavior, they can result in the theft of valuable intellectual property, including proprietary training techniques, model architectures, or fine-tuning investments. Exposed inference endpoints and publicly accessible AI services increase the risk by giving attackers opportunities to interact with models at scale. From an exposure management perspective, organizations must understand which AI services are externally reachable and whether they expose functionality that could facilitate unauthorized model access or replication.
Shadow AI and unauthorized AI deployments
Shadow AI refers to the use of AI tools, services, agents, or integrations that operate outside approved governance, security review, or asset management processes. Employees and developers can quickly deploy AI-powered applications, connect third-party AI services to enterprise data, or expose AI endpoints without informing security teams. This creates visibility gaps that leave organizations unaware of where sensitive data is flowing or which AI assets are externally accessible.
Shadow AI is fundamentally an exposure problem because organizations cannot secure AI assets they do not know exist, making continuous discovery essential as AI deployments proliferate.
Exposed AI APIs and inference endpoints
AI APIs and inference endpoints create new internet-facing attack surfaces that attackers can discover and probe just like traditional applications. Misconfigured, undocumented, or forgotten AI services may expose sensitive data, enable unauthorized model access, facilitate model extraction, or serve as entry points for prompt injection and other attacks. The challenge is compounded by the speed at which AI services are deployed and modified, often outside normal asset inventory processes.
This makes the continuous discovery and validation of externally reachable AI endpoints crucial. It’s the only way of getting ahead of exposures that pose real, exploitable risk; relying solely on periodic reviews doesn’t suffice given the speed at which modern AI infrastructure changes.
Insecure AI agents with excessive permissions
AI agents introduce unique security risks because they can autonomously access data, interact with systems, and execute actions on behalf of users. When agents receive excessive permissions or connect to sensitive resources without proper controls, attackers can exploit prompts, inputs, memory, or delegated privileges to manipulate agent behavior. For instance, an over-privileged agent may gain access to databases, cloud resources, internal applications, or APIs far beyond what is necessary to perform its intended function.
Effective AI agent security requires strict least-privilege access, continuous monitoring, and visibility into what systems agents can reach and what actions they can perform. It’s also critical to discover and secure resources that help power AI agents, such as MCP servers.
The role of AI risk management in cybersecurity
AI risk management has become a key pillar of modern cybersecurity because a structured risk management framework helps organizations govern the growing role and complexity of AI systems in business environments.
Historically, most AI technologies that organizations deployed took the form of descriptive or predictive analytics tools – like those that could forecast financial results or recommend products to website visitors.
Those AI systems are still in use. But over the past several years, enterprises have embraced other, more novel types of AI – namely, generative AI and agentic AI technologies. These introduce additional cybersecurity challenges because they often work across multiple business functions or domains, have access to large amounts of data, and (in the case of AI agents) can potentially carry out actions autonomously. More traditional types of AI systems were simpler and didn’t pose this level of risk across the AI lifecycle.
This is why cybersecurity strategies must evolve to address the unique challenges posed by modern AI. In addition to deploying controls that can detect and mitigate the unique types of AI security risks described above, businesses must also ensure that they are able to discover AI assets and identify AI-related risks associated with them continuously and in real time across the AI lifecycle while protecting trustworthy AI systems.
AI risk management vs. traditional application security
To be more specific about why cybersecurity strategies must adapt for the AI era, it’s helpful to understand how managing AI risks differs from managing risks in conventional applications – meaning those that aren’t powered by AI models.
Traditional applications are deterministic. In other words, they operate based on code that tells them what to do. In these applications, the same input will always result in the same output.
Security flaws in traditional apps usually arise from flaws within the applications’ internal code or from configuration errors that cause them to behave in unexpected ways. Thus, the main focus of traditional application security is on detecting code vulnerabilities and misconfigurations.
Modern AI, however, is a fundamentally new domain from a security perspective. A key reason why is that AI systems are probabilistic and non-deterministic, with ethical implications alongside security consequences. Rather than relying on instructions in the form of code to govern their behavior, they operate by detecting patterns within vast quantities of data. This characteristic makes it challenging to predict exactly how an AI system will behave – and by extension, it opens the door to novel types of security risks, like data poisoning attacks (which manipulate AI model behavior by introducing malicious patterns into the data that models interpret) and prompt injection (which takes advantage of the probabilistic nature of AI models to “trick” them into revealing sensitive data or performing malicious actions because they lack explicit instructions telling them not to do these things).
On balance, it’s worth noting that there is overlap between traditional application security and AI security. For example, AI models, like applications, can be subject to misconfigured access controls that allow users to access data that should not be visible to them. But AI introduces novel security threats and other identified risks – which is why businesses need tailored controls that extend beyond conventional application security to secure modern AI.
Tips from the Expert
Rob Gurzeev, CEO and Co-Founder of CyCognito, has led the development of offensive security solutions for both the private sector and intelligence agencies.
Here are our tips on managing modern AI risks effectively and efficiently, and in alignment with the organization’s risk tolerance level:
- Implement continuous threat exposure management (CTEM): CTEM processes help ensure that businesses are able to keep pace with AI security risks by detecting and mitigating threats in real time. Continuous discovery and assessment of security exposures is the only way to stay a step ahead of threat actors in the AI era and address cyber threats and other associated risks.
- Validate and contextualize AI risk exposures: To deploy cybersecurity resources most efficiently, validate AI security risks by confirming that they represent real threats based on the technical and business context surrounding them. Automated exposure mapping and scenario planning can help by streamlining the process of understanding potential attack paths and potential risks.
- Use AI to augment defensive security operations: AI isn’t just a new type of resource for organizations to protect. It can also serve as a weapon that drives defensive security operations. Indeed, in a world where attackers can use AI to do things like discover vulnerabilities faster than ever, it’s imperative for defenders, too, to leverage AI to accelerate risk detection and assessment.
- Optimize security investments around risk and business impact: Not all AI security issues pose the same level of risk. When deciding what to prioritize, it’s critical to factor in business context and the organization’s risk tolerance appetite; for example, factors such as the sensitivity level of the data handled by an AI system or the importance of an AI service to business operations should inform decisions about AI risk management.
Integrating AI risk management into the SDLC
Protecting AI systems at scale requires the ability to integrate AI risk management directly into the software development lifecycle (SDLC). Periodic security audits or testing of AI systems isn’t enough because under that approach, there is a chance that attackers could exploit vulnerabilities in AI systems before the business becomes aware of them.
A better strategy is to ensure that whenever a new AI system, or an update to an existing system, is prepared for deployment, testing occurs to identify risks. As part of the SDLC, each AI project should include a risk assessment before deployment. Ongoing AI risk management should continue after release to track AI risks and model behavior over time.
This becomes possible when organizations implement real-time, automated penetration testing and exposure management tools that allow them to probe for and assess AI security risks systematically. That said, human-in-the-loop oversight remains necessary after deployment to correct model deviations such as model drift.
AI risk management frameworks
A growing number of risk management frameworks can help organizations manage AI risks.
One key example is NIST AI RMF, a voluntary framework released in January 2023. Its four core functions – Govern, Map, Measure, and Manage – can structure the way organizations approach the processes of discovering and assessing AI risks.
ISO 42001, a slightly newer AI risk management framework, offers similar guidance for structuring AI exposure management activities. Unlike NIST AI RMF (which operates on a self-attested basis), ISO 42001 is a certifiable system that includes audit-ready controls, making it particularly valuable for organizations that need to demonstrate effective AI risk management to auditors, partners, or customers.
Common AI risk management challenges
Managing AI risks is important, but it’s not always easy because organizations face operational, governance, and compliance challenges. Challenges like the following can impede the process:
- AI sprawl: As more and more AI tools and services enter IT environments, keeping track of all of them – let alone testing them for risks – can be difficult. This makes continuous, real-time discovery of AI systems and exposures especially critical.
- Limited AI expertise: Many organizations lack teams with extensive experience in developing or securing AI systems. This challenge, coupled with unclear AI risk owners, can slow risk management efforts and impede organizations’ ability to detect and respond to AI risks.
- Internal vs. external AI security: Businesses must check for risks within AI systems they develop themselves, as well as third-party AI applications and services. However, the approaches they use for each type of AI asset often have to vary because the levels of access and control they have are limited in the case of third-party AI.
- Missing or inadequate documentation: As businesses have rushed to embrace modern AI over the past few years, many have implemented systems faster than they have documented them. This can lead to a lack of visibility, making it harder to plan effective AI security tests. It also underscores the need to establish a cross-functional AI governance committee, with compliance teams and data scientists alongside technical, legal, and business leaders responsible for documentation and oversight. This is also an area where automated asset discovery and risk management tools can come in handy by collecting details and context about AI systems, then using them to drive effective tests.
The role of AI risk management in validated exposure management
AI risk management plays a critical role in validated exposure management by helping organizations identify, assess, and prioritize the risks introduced by AI systems across their external attack surface.
As enterprises deploy AI models, agents, APIs, MCP servers, and third-party AI services, they create new avenues for attackers to exploit. Traditional AI risk management frameworks focus on governance, policies, and controls, with risk frameworks and risk management practices providing the foundation that validated exposure management operationalizes. It extends these efforts by continuously discovering AI assets, evaluating their security posture, and determining whether identified weaknesses can actually be exploited in real-world attack scenarios to help organizations manage AI risks. This approach helps organizations move beyond theoretical risks and focus remediation efforts on exposures that represent genuine business risk.
Validated exposure management also provides an ongoing mechanism for verifying that AI security controls remain effective as environments evolve. AI deployments often change rapidly, creating shadow AI, exposing new APIs, or introducing previously unknown dependencies and integrations.
By continuously monitoring externally exposed AI assets and using exploit intelligence to validate exploitability from an attacker’s perspective, organizations can identify high-risk exposures such as vulnerable AI endpoints, overprivileged agents, insecure MCP servers, and misconfigured cloud resources before they are leveraged in an attack.
In this way, AI risk management establishes the management framework for managing AI-related threats, while validated exposure management provides the operational visibility, continuous validation, and risk mitigation strategies needed to ensure those risks are effectively controlled in practice.
Best practices for managing AI security risks
The following best practices can help organizations stay ahead of AI security risks:
Automate AI security
The scale and constantly changing nature of AI systems make automation critical within the context of risk management. Enterprises should leverage automated discovery, monitoring, and validation processes to identify AI assets, detect misconfigurations, continuously assess AI-related exposures, and track AI risks consistently across changing environments. Automation helps organizations keep pace with the rapid deployment and evolution of AI systems while reducing security blind spots and addressing emerging risks.
Integrate AI security controls into the SDLC
Embedding AI security requirements, testing, governance checks, and responsible AI practices throughout the software development lifecycle to identify risks before deployment. This approach helps ensure that AI applications, models, agents, and integrations are designed and released with security in mind, supports responsible AI development, and helps mitigate AI risks before launch as part of the practical implementation of an AI risk program.
Document AI resources
Maintaining an inventory of AI models, agents, APIs, data sources, integrations, and supporting infrastructure improves visibility and governance. Comprehensive documentation also helps security teams understand where AI is deployed, how it operates, and what risks it may introduce.
Minimize AI data exposure
The less data AI systems can access, the less potential harm attackers can cause if they breach those systems, improving data protection by minimizing AI data exposure. To this end, restrict AI models and agents from ingesting or processing sensitive, proprietary, or regulated data unless their intended use case requires them to do so, supporting regulatory compliance and reducing regulatory exposure. Applying least-privilege principles and strong data governance reduces the potential impact of data leakage, unauthorized access, and AI-driven exfiltration, while also helping address associated risks tied to unauthorized access and AI-driven exfiltration.
Securing AI systems with CyCognito
A risk management framework tells you which AI risks to govern. It does not tell you which of your AI systems are internet-facing right now, or which of those exposures an attacker could actually reach. CyCognito is a leading external attack surface management platform that continuously discovers and validates every AI-connected asset your organization exposes, from inference endpoints to MCP servers, starting from nothing more than your organization’s name.
- Discovers exposed AI assets your inventory misses, including model APIs, inference endpoints, MCP servers, and agent infrastructure, without seeds, agents, or a prior asset list
- Surfaces shadow AI deployments that bypassed procurement and security review, closing the visibility gap a risk management program assumes you already have
- Continuously validates whether discovered AI exposures are actually exploitable, so remediation targets genuine business risk instead of theoretical severity
- Prioritizes findings using attacker reachability and business context, aligning triage with your organization’s risk tolerance rather than severity scores alone
- Routes validated findings to the right owners and tracks them through to verified closure
Organizations using CyCognito typically find their attack surface is up to 20x larger than previously inventoried. In an environment where AI endpoints are spun up faster than they are catalogued, that unmonitored gap is exactly where attackers look first.
If you want to see CyCognito in action, click here to schedule a 1:1 demo.
