Artificial intelligence is no longer a futuristic technology confined to academic research or experimental startups. It is now embedded in everything from financial systems to healthcare diagnostics, logistics, energy infrastructure, and national defense mechanisms. This rapid deployment has caught the attention of governments, regulators, and enterprises around the world, but a critical element has been left behind: security. Despite the explosive adoption of AI technologies, the prevailing mindset surrounding AI security remains dangerously outdated. Businesses continue to treat AI systems as if they were simply another form of traditional software. This leads to the dangerous assumption that conventional security protocols, such as patch management and vulnerability disclosure mechanisms, are sufficient to secure AI systems. But AI is not just software. It learns, adapts, and behaves in ways that defy the static nature of traditional code. This fundamental misunderstanding lies at the heart of why AI security is the next major risk frontier—and why most industries are not prepared for it.
The most common mistake security professionals make is assuming that vulnerabilities in AI systems can be tracked and mitigated using the same models used for traditional software. One of the most prominent frameworks in the world of cybersecurity is the Common Vulnerability Exposure (CVE) system. This approach relies on identifying specific, reproducible flaws in software code, assigning them a severity score, and disseminating the information to stakeholders for remediation. It is a well-established and relatively efficient method for addressing risks in software products. However, CVEs are based on the assumption that vulnerabilities are deterministic and that software behaves predictably when those vulnerabilities are triggered. AI breaks that assumption. AI systems do not consist solely of fixed rules or lines of code. Instead, they operate through models trained on vast datasets, often opaque or proprietary, which introduce behaviors that are not only unpredictable but also difficult to reproduce or validate. This creates a fundamental mismatch between how AI fails and how we traditionally report and fix software flaws.
From Code Vulnerabilities to Behavioral Instability
In traditional software systems, a vulnerability is typically a mistake in the code—like a buffer overflow, SQL injection, or improperly validated input—that an attacker can exploit to compromise the system. Once discovered, these vulnerabilities can often be patched by correcting the flawed code, recompiling the application, and issuing an update. That process, while not perfect, is well-understood and supported by decades of practice and tooling. In contrast, a vulnerability in an AI model may not be related to the code at all. Instead, it could arise from something as abstract as the training data. For instance, a malicious actor could introduce poisoned data into the training pipeline, subtly shifting how the model behaves under certain conditions. Alternatively, an attacker might exploit a model’s inability to generalize safely outside of its training distribution, leading it to generate harmful or misleading outputs. These are not traditional bugs. They are emergent properties. And they often defy easy detection.
Another complication is that AI models are often non-deterministic. Given the same input, an AI model may not produce the same output every time, especially when probabilistic techniques are used during inference. This makes vulnerability reproduction extremely difficult. It also means that even if a suspicious behavior is identified, it may be difficult to prove that it constitutes a security threat according to legacy frameworks. In some cases, these behaviors are dismissed altogether as anomalies or edge cases rather than genuine risks.
The limitations of the CVE framework become painfully obvious in such situations. The system is simply not equipped to deal with dynamic, context-sensitive, and probabilistically-driven vulnerabilities. Attempting to force AI vulnerabilities into this mold results in missed threats, inadequate responses, and ultimately a growing blind spot in our security posture.
The Illusion of Security Through API Control
Adding another layer of complexity is the way AI systems are accessed. Most organizations interact with AI models through Application Programming Interfaces. These APIs abstract the underlying complexity of the model and offer a clean, predictable way for developers to build AI-powered features. While this abstraction is useful from an engineering perspective, it creates an illusion of security. Developers and business leaders assume that if the API is protected by authentication, rate limiting, or encryption, then the AI model behind it must also be secure. But this is a false sense of safety.
APIs are only as secure as the models and systems they expose. They can be manipulated, misused, or exploited in ways that bypass traditional API protections. In fact, according to several cybersecurity agencies and research institutions, APIs are increasingly becoming the primary attack vector for AI-related breaches. Attackers can use APIs to conduct prompt injection attacks, model inversion, data extraction, and other forms of adversarial manipulation. None of these attacks requires breaching the API itself. Instead, they rely on exploiting the model’s behavior, which often goes unmonitored and unmitigated.
To illustrate this point, researchers at various cybersecurity labs have demonstrated how sensitive information can be extracted from large language models via carefully crafted queries. These models, trained on massive and often uncurated datasets scraped from the internet, may inadvertently memorize and regurgitate private or sensitive data. The issue isn’t that the API was poorly secured; it’s that the model behaves in unexpected ways when prompted in certain patterns. The current generation of security tools is largely blind to these behavioral vulnerabilities. Firewalls and intrusion detection systems are not designed to detect adversarial prompts. Static analyzers do not examine training data provenance. Code scanners cannot assess the ethical or operational risk of a model’s emergent behavior. In short, conventional tooling leaves vast blind spots in AI systems, and these blind spots are where attackers thrive.
Invisibility of AI Vulnerabilities and the Reporting Crisis
Even when AI-specific vulnerabilities are discovered, there is often no clear or standardized way to report them. Many AI products are built using a complex mix of open-source libraries, commercial APIs, proprietary models, and third-party integrations. This patchwork of components makes it difficult to determine who is responsible when something goes wrong. Unlike traditional software, where version numbers and patch histories provide a clear lineage, AI models are frequently opaque, undocumented, and subject to change without notice.
Worse still, many AI vendors do not maintain formal security disclosure channels. Independent researchers attempting to report vulnerabilities in AI models often find themselves facing legal threats, ignored emails, or vague responses dismissing their concerns. Some companies claim that since the issue is not a conventional software vulnerability, it falls outside the scope of their security policy. This discourages responsible disclosure and creates a chilling effect on independent research. In turn, this results in a dangerous accumulation of unreported and unresolved threats.
Even when vendors do acknowledge AI vulnerabilities, they often struggle to classify or score them. How do you assign a severity score to a model that subtly misclassifies certain inputs under adversarial pressure? Or a chatbot that generates harmful advice only when coaxed through a 20-step prompt sequence? These vulnerabilities do not fit neatly into existing severity taxonomies. As a result, they often go untracked and unresolved.
This lack of reporting standards and disclosure processes leaves security teams in the dark. They cannot defend against threats they do not understand or anticipate. And without clear visibility into how AI systems fail, they are unable to prioritize their defensive efforts or allocate resources effectively. The result is a widespread underestimation of the risks associated with AI adoption—a ticking time bomb hidden beneath a layer of reassuring but inadequate legacy frameworks.
The Need for a Paradigm Shift in Security Thinking
The root of the problem is not just technical—it is conceptual. The cybersecurity community has spent decades refining its understanding of static software vulnerabilities, but it has only begun to scratch the surface of AI security. Most tools, processes, and even professional mindsets are built around the assumption that software is deterministic and that its behavior can be fully understood through its source code. AI contradicts that assumption at every level. Its behavior is a function not just of its code, but also of its data, its architecture, its training objectives, and its post-deployment environment. This means that a vulnerability could be latent, subtle, and only triggered in specific contexts that are not obvious during testing or inspection. A traditional software vulnerability, once found, can be fixed and is unlikely to re-emerge unless reintroduced. But in AI, the same issue could resurface under different conditions, due to retraining, fine-tuning, or exposure to adversarial input. This is a moving target that demands a fundamentally different approach to security.
The question, then, is not how to make AI fit the traditional models of cybersecurity but how to reimagine security from the ground up in ways that reflect the unique properties of AI systems. This requires not only new tools and frameworks but also a new mindset—one that accepts complexity, embraces uncertainty, and prepares for behavior-based vulnerabilities that evolve. In the next section, we will explore how AI vulnerabilities manifest across the supply chain and why current responses are inadequate for identifying and mitigating these systemic risks.
The Invisible Threats in the AI Supply Chain
One of the most misunderstood aspects of modern AI development is how these systems are created. Unlike traditional software that is typically written line by line by developers, AI models are constructed through a highly complex and layered process. At the center of that process is a combination of training data, pre-trained models, open-source libraries, proprietary integrations, and API wrappers. In essence, AI systems are assembled from a vast network of interdependent parts. Each of these components introduces potential vulnerabilities, but few organizations fully track or manage these dependencies.
This creates a sprawling, opaque supply chain where accountability becomes nearly impossible to enforce. A single AI system may rely on dozens or even hundreds of components sourced from external repositories, many of which are poorly documented, unmaintained, or insecure. These components can include open-source machine learning frameworks, pre-built datasets with unknown provenance, community-contributed model weights, and containerized environments pulled from public registries. As each layer is stacked on top of another, the overall complexity of the system increases—and so does its vulnerability.
The software industry has dealt with similar challenges in traditional application development. This led to practices such as Software Bill of Materials (SBOM), which catalogs all the components included in a software product. But in the AI domain, the equivalent—known as the AI Bill of Materials (AIBOM)—is rarely used, let alone standardized. Most AI vendors do not publish a detailed breakdown of what their models include, where the data came from, what third-party tools were involved, or what license restrictions apply. Without this transparency, security professionals cannot audit or monitor the full scope of risk embedded in an AI product.
Dynamic Models, Static Security
A core problem within AI supply chains is the dynamic nature of AI models. Unlike traditional software, which typically remains static between updates, AI models can change constantly. Some organizations implement automated retraining loops, where models are updated periodically based on new data, user feedback, or performance metrics. This process is often opaque and poorly monitored. Every retraining introduces potential new vulnerabilities, especially if adversarial or poisoned data is introduced during the update cycle.
Even without retraining, models may exhibit drift. Model drift occurs when the statistical properties of input data change over time, leading to shifts in the model’s behavior. A model trained on a dataset from 2021 may no longer perform reliably when exposed to data in 2025. In some cases, this leads to performance degradation. In others, it opens the door to subtle, emergent security issues. A model that once resisted prompt injection may become vulnerable after a feedback-driven tuning process. Yet security protocols for AI rarely account for this.
What makes the issue even more challenging is the lack of version control and observability in AI pipelines. Developers may fine-tune a model without properly documenting the data used, the parameters changed, or the security implications of the update. This undermines reproducibility and makes auditing extremely difficult. Without strong versioning discipline and logging practices, it becomes impossible to determine when a vulnerability was introduced, what triggered it, or how to reverse it. This dynamic instability—combined with static security controls—creates blind spots throughout the AI lifecycle.
Ownership and Accountability Breakdown
Another foundational challenge with AI supply chains is the breakdown of ownership and accountability. In traditional software, vendors typically control the full stack. If a vulnerability is discovered, the software provider is responsible for issuing a patch and notifying affected users. In the AI space, however, the responsibility is often diffused across multiple actors, many of whom may not even be aware of their role in the product’s risk profile.
Consider a situation in which a large enterprise integrates a third-party LLM through an API and fine-tunes it with proprietary customer data. If a security issue arises—such as data leakage, biased outputs, or model inversion—who is at fault? The original model provider? The enterprise that performed the fine-tuning? The dataset vendor that provided corrupted training data? The open-source framework that powered the pipeline? In most cases, no single party is equipped—or willing—to assume full responsibility. This leads to inconsistent responses, a lack of disclosure, and inadequate remediation.
The problem is made worse by the fact that many AI vendors, especially those offering model-as-a-service products, treat their model internals as proprietary and confidential. This makes independent audits or vulnerability disclosures difficult, if not impossible. Researchers who attempt to investigate potential risks are often met with legal pushback, vague denials, or outright hostility. Meanwhile, customers using these AI services remain unaware of the risks they are exposed to.
This accountability gap is not just a technical problem; it’s a regulatory and ethical one. As AI systems become embedded in critical infrastructure—such as healthcare diagnostics, legal decision support, and national security systems—the lack of clear responsibility could have devastating consequences. Without systemic change, we risk building a technological future where security failures are inevitable, untraceable, and unfixable.
Barriers to Reporting and Disclosure
One of the most damaging aspects of the current AI security landscape is the absence of a structured, standardized way to report vulnerabilities. In the traditional software world, when a researcher discovers a security flaw, there are well-established paths for responsible disclosure. Organizations like MITRE and NIST help catalog and classify these vulnerabilities through the CVE system, ensuring that the information is distributed appropriately and remediation efforts can begin.
This system breaks down entirely in the AI space. Most AI vendors do not have formal vulnerability disclosure programs. Few accept bug reports related to model behavior, and even fewer assign internal resources to investigate them. When researchers identify serious flaws—such as models leaking sensitive training data, producing harmful recommendations, or failing in adversarial conditions—they are often left with no clear path to alert the vendor. Even when disclosure does occur, the lack of a CVE-style framework for AI means the vulnerability often goes untracked, unscored, and unaddressed.
Worse, some vendors actively discourage disclosure. There are cases where security researchers have faced legal threats or had their accounts suspended after reporting flaws in AI products. This creates a chilling effect across the research community. Ethical hackers are disincentivized from investigating AI products, and public trust in vulnerability reporting erodes. This secrecy culture might protect short-term business interests, but it leads to long-term harm across the ecosystem.
What is needed is a complete rethinking of how we treat AI vulnerabilities. Vendors must acknowledge that adversarial behavior, data leakage, prompt manipulation, and emergent biases are valid security concerns, not mere performance quirks. They must create secure channels for disclosure, encourage external testing, and collaborate with regulators and researchers to establish standards for severity assessment, triage, and response. Until that happens, AI systems will continue to operate with significant hidden risk.
The Fragile Illusion of “Safe” AI Products
For many organizations, the default response to AI security concerns is denial. Vendors downplay issues, cite proprietary protections, or argue that the risks are hypothetical. Customers assume that if a product is commercially available, it must be secure. Regulators hesitate to act without clear definitions or public pressure. This creates a fragile illusion of safety—a belief that AI systems are robust simply because they are widely adopted.
This illusion is shattered when real-world incidents occur. From chatbots that promote self-harm to models that leak private data or reinforce harmful stereotypes, the evidence is growing that current AI security practices are inadequate. These failures are not isolated bugs. They are symptoms of a broader structural weakness: a failure to align AI development with modern security principles.
The concept of “secure by design,” widely adopted in traditional software engineering, is largely absent in AI development. Security is often bolted on as an afterthought, rather than embedded into the design and training phases. Threat modeling is rarely conducted with an AI-specific lens. Training data is not routinely audited for poisoning or bias. Monitoring systems do not track behavioral drift or adversarial response patterns. Without these practices, we are not just ignoring known threats—we are creating the conditions for new ones to flourish.
The Capability Gap: Why We’re Not Equipped to Handle AI Threats
AI Threats Are Unlike Traditional Vulnerabilities
Most organizations approach cybersecurity with a fairly robust set of tools. Vulnerability scanners, intrusion detection systems, endpoint monitoring, penetration testing, threat modeling, and red-teaming are all part of the arsenal used to detect, analyze, and mitigate risks in traditional software. But these tools were designed with a specific kind of system in mind: software that follows deterministic rules, where vulnerabilities are the result of coding errors, misconfigurations, or outdated dependencies.
AI systems do not operate in this way. Their vulnerabilities are often behavioral, probabilistic, and context-dependent. An AI model might behave safely under one set of inputs and maliciously under another, with no change to the underlying code. The same prompt may elicit different responses depending on subtle input tweaks or the model’s internal state. And many attacks—such as prompt injections, jailbreaks, or model inversions—leave no trace in logs or metrics, making them invisible to standard security tools.
This means that traditional security scanners and testing suites are fundamentally unfit for identifying the majority of AI-specific risks. A static code analyzer cannot detect whether a language model will reveal sensitive information when coaxed. A penetration test designed for server infrastructure won’t uncover if a model is vulnerable to adversarial inputs. Endpoint protection won’t detect that a recommendation algorithm is being manipulated through feedback loops. Security teams are operating with tools that were never intended to analyze adaptive, data-driven behavior.
The result is that even large, well-resourced organizations are blind to some of the most critical vulnerabilities in their AI systems. Without the right testing frameworks, logging mechanisms, or behavioral baselines, these systems become security black boxes—complex, opaque, and largely unexamined.
A Shortage of AI-Literate Security Professionals
Just as traditional security tools aren’t suited for AI systems, traditional security teams often aren’t either. The domain of AI security requires knowledge that spans machine learning theory, statistical modeling, adversarial attacks, data provenance, and behavior-driven risk assessment. Most cybersecurity professionals have not been trained in these areas. And most data scientists, in turn, are not trained to think like security professionals.
This creates a major gap in expertise. Security engineers may know how to defend a network but have no experience with concepts like model poisoning or latent bias exploitation. Machine learning engineers might understand how to build and train models, but are unfamiliar with prompt injection, data leakage vectors, or how to conduct a red team test on an AI agent. The two groups operate in silos, each with partial visibility into the threat landscape, and neither fully prepared to defend against a new class of AI-native attacks.
Some large organizations are beginning to build cross-disciplinary teams that combine AI and security expertise. But this remains the exception, not the rule. Most companies—especially those outside the tech sector—lack even a single dedicated AI security professional. As a result, major decisions about AI deployment are made without thorough security vetting, and discovered issues are often addressed in an ad hoc, inconsistent manner.
The industry also lacks formal training programs, certifications, or career paths for AI security. A junior security analyst may know how to conduct a penetration test or respond to a CVE disclosure, but they’re unlikely to know how to detect signs of model degradation, respond to a jailbreaking attack, or validate the provenance of training datasets. Until educational institutions and professional bodies catch up with the need for AI-literate defenders, this gap will only widen.
Testing AI Requires an Entirely New Playbook
Security testing is one of the most foundational practices in cybersecurity. Before releasing software into the wild, responsible vendors subject it to rigorous testing, including unit tests, integration tests, static analysis, dynamic analysis, and real-world simulations of adversarial behavior. But these approaches rely on the premise that software behavior is predictable and reproducible. This is not the case with AI.
To properly test AI systems, new approaches are needed. One such method is adversarial testing—intentionally probing models with specially crafted inputs designed to cause failure or misbehavior. This could involve prompt injection for LLMs, adversarial image perturbations for vision systems, or manipulated feedback loops for recommendation engines. These attacks often require a deep understanding of the model’s architecture, training regime, and response patterns.
Another essential method is behavior monitoring—continuously tracking how an AI system behaves in production to detect anomalies, regressions, or signs of manipulation. But this is far more complex than monitoring logs or system performance metrics. It requires developing behavioral baselines for AI outputs, statistical tools to detect drift or inconsistency, and alerting systems that can identify subtle shifts in model reasoning.
Finally, threat modeling for AI must go beyond standard frameworks. Traditional threat models focus on actors, assets, and attack vectors. In AI systems, we must model not only how an attacker might access the system but also how they could exploit its training data, manipulate its environment, influence its learning process, or coerce it into harmful outputs. This kind of modeling is still in its infancy and lacks standardization across industries.
Without these new testing paradigms, AI systems are released with little to no understanding of how they might fail under pressure. We cannot rely on past assumptions or legacy practices. We need entirely new methodologies that treat AI as a novel domain, with its attack surfaces and risk profiles.
The Danger of False Confidence
One of the most dangerous outcomes of this capability gap is the illusion of security. Because AI systems are often wrapped in familiar interfaces—APIs, dashboards, command lines—they give the impression of stability and predictability. Engineers assume that if the system responds correctly under normal testing, it must be secure. Executives believe that if a model performs well on benchmarks, it must be reliable. Customers trust that if a product is commercially available, it must be safe.
This false confidence is extremely risky. Many real-world AI failures have occurred not because systems were broken, but because they operated just well enough to pass a cursory evaluation—until one day, they didn’t. An LLM may answer most questions correctly, but suddenly leak proprietary code if prompted cleverly. A vision model may classify images accurately, but completely fail on inputs containing specific adversarial noise. A chatbot may behave politely during testing, but adopt toxic behavior when released to the public.
Without rigorous adversarial testing, robust behavioral monitoring, and AI-specific red teaming, these failures go undetected. The risk is further compounded when organizations scale up deployment across sensitive domains—financial fraud detection, medical diagnosis, and autonomous systems—without first validating that the model behaves safely in edge cases or under attack. The cost of these oversights will not just be financial or reputational. In some industries, they could be life-threatening.
Legal and Ethical Barriers to Capability Development
Even when organizations want to invest in AI security capabilities, they often encounter legal or ethical roadblocks. Many AI vendors impose terms of service that prohibit reverse engineering, probing, or adversarial testing of their models. Researchers who try to test model robustness may be accused of violating platform policies or even face legal threats. This discourages security innovation and prevents the development of shared tools and standards.
Ethical barriers also play a role. Some security professionals are hesitant to engage in adversarial testing because it involves intentionally trying to break a system, manipulate its behavior, or trigger harmful outputs. While this kind of testing is essential for identifying vulnerabilities, it must be conducted responsibly and under controlled conditions. Without clear ethical guidelines and professional standards for AI red teaming, many organizations avoid it altogether out of fear of unintended consequences.
This creates a paradox. We need better tools and methods to secure AI systems, but the very rules designed to protect those systems often inhibit the development of those tools. This must change. Organizations must recognize that responsible adversarial testing is not a threat to safety—it is a prerequisite for it. Legal frameworks must evolve to protect ethical security research, and vendors must adopt transparency policies that allow their systems to be scrutinized by experts without retaliation.
The Need for a Purpose-Built AI Vulnerability Framework
The software industry has spent decades building structured processes for managing security risks. Chief among these is the Common Vulnerabilities and Exposures (CVE) system, which assigns standardized identifiers to security issues and allows the community to track, prioritize, and respond to them. This model has served traditional software well. But as explained earlier, it was never designed to accommodate the behavioral, probabilistic, and emergent threats posed by AI.
AI-specific vulnerabilities cannot always be described in binary terms. Many are not triggered by code defects but by data flaws, model behaviors, input manipulations, or poorly defined operational boundaries. An AI system may leak sensitive information only under certain inputs, exhibit bias in specific regions, or behave inconsistently depending on feedback patterns. These issues do not cleanly fit within the current CVE format, which expects a discrete flaw with a reproducible exploit and a clear fix.
What is needed is an entirely new vulnerability framework tailored to AI systems—one that incorporates behavioral testing, context-dependent risks, and the evolving nature of learning-based models. This framework would not only track individual bugs or misconfigurations but also systemic risks like unsafe training data, insecure model outputs, or adversarial robustness failures. It would prioritize vulnerabilities based not only on the likelihood of exploitation, but on the potential societal impact and system scale.
To succeed, such a framework must have industry-wide support, involve cross-functional expertise from security, machine learning, and ethics, and be integrated into existing disclosure pathways. AI-specific identifiers could be issued by a new independent body or a modified version of the existing CVE ecosystem, but with adapted criteria, review boards, and public communication practices. Crucially, this process must encourage disclosure—not punish it—and allow researchers to report AI vulnerabilities without fear of legal retaliation or dismissal.
Building an AI Bill of Materials (AIBOM)
In traditional software development, the concept of a Software Bill of Materials (SBOM) has gained traction as a way to ensure supply chain transparency. An SBOM lists the components, libraries, and dependencies that make up a software product, making it easier to identify where vulnerabilities originate and whether a system is affected by known flaws.
In the same way, AI systems need an AI Bill of Materials (AIBOM). This document would provide a detailed, standardized record of what went into the construction of an AI model, including training datasets, third-party components, data preprocessing steps, model architectures, fine-tuning processes, hyperparameters, and system dependencies. Where applicable, it would also include the sources of data (scraped from public websites, proprietary corpora, user input, etc.) and whether those sources were verified, filtered, or audited.
An AIBOM serves multiple purposes. It allows security professionals to assess whether a system is vulnerable to specific known flaws in data or model components. It gives researchers a path to reproduce and analyze issues. It supports compliance with data governance, privacy, and intellectual property standards. And most importantly, it enhances transparency in a domain that is currently plagued by black-box development and vague accountability.
AIBOMs should be mandated for AI systems used in critical infrastructure, national security, healthcare, and financial services. For other sectors, they should at least be a best practice. Without them, organizations will continue to run AI models that they do not fully understand, cannot trace, and cannot effectively audit or secure.
Regulatory Urgency and the Risk of Inaction
Governments and regulators are only beginning to grasp the scale and uniqueness of AI security risks. Most existing regulatory frameworks for cybersecurity make no specific mention of machine learning systems or their behavioral attack surfaces. Compliance requirements are written with deterministic software in mind—assumptions that break down completely when applied to LLMs, vision models, or generative AI agents.
Several governments have issued voluntary guidance on AI safety and transparency. The UK AI Security Code of Practice, for example, lays out useful principles. But as noted, these are not mandatory and have no enforcement mechanisms. In critical industries, voluntary compliance will always be insufficient. What’s needed are binding regulatory standards that define what secure AI development looks like, impose minimum disclosure and documentation rules, and incentivize vendors to support independent testing and security research.
A regulatory system that recognizes AI-specific risks should require vendors to:
- Allow third-party red teaming of their models
- Disclose known vulnerabilities and mitigation steps.
- Provide AIBOMs for the critical system.s
- Comply with AI-specific vulnerability disclosure timelin.es
- Cooperate with coordinated disclosure processes.
These regulations must be adaptable. AI evolves rapidly, and static rules will quickly become obsolete. Regulators should work closely with the security community, model developers, and industry coalitions to ensure that frameworks are updated in real-time and reflect current understanding of the AI threat landscape.
Inaction poses serious risks. If left unchecked, AI security failures will grow more frequent and more impactful, leaking sensitive data, enabling fraud, generating disinformation, or even compromising physical systems. The regulatory window for proactive intervention is narrowing, and every delay makes the consequences harder to contain.
Toward a Secure by Design AI Culture
The long-term solution to AI security will not come from patching models after the fact. It will come from embedding security into the design, development, and deployment lifecycle of AI systems—what the software industry calls “secure by design.”
This cultural shift must start at the level of organizational planning. AI projects should be threat-modeled from day one, with security goals defined alongside accuracy and performance metrics. Training data should be audited not only for quality but for safety, provenance, and the potential to introduce bias or adversarial vectors. Model architectures should be selected and tested with robustness in mind, and deployment environments should include behavioral monitoring from the outset.
Security must also be built into the toolchains and platforms that developers use. Many AI projects rely on shared libraries, open-source models, or third-party APIs. These supply chain components must themselves be hardened, versioned, and verified. Access to training pipelines and production models should follow the principle of least privilege. Logging should capture not only system calls but also AI-specific telemetry—model inputs, outputs, and anomalies.
Crucially, the people building and deploying AI need to be security literate. Secure by design only works if developers know what to watch for. This means that machine learning curricula must include security modules. Corporate security teams must invest in training that covers adversarial attacks, AI red teaming, and behavioral monitoring. And leadership must recognize that investing in AI security is not optional—it is an essential part of product development.
Collaboration and Collective Defense
AI security is not a problem any one company or government can solve alone. The attack surface is too vast, the technologies too opaque, and the adversaries too adaptable. What’s needed is a shared defense model—an ecosystem where vulnerability information is responsibly shared, where red teaming is collaborative rather than competitive, and where AI developers and security professionals work side-by-side to raise the baseline.
This starts with trust. Vendors must stop treating security disclosures as reputational threats and start treating them as opportunities to improve. The research community must be supported with safe harbor protections. Independent labs should be funded to stress-test commercial models, and governments should serve as neutral coordinators of information exchange, not just regulators.
Information sharing groups focused specifically on AI security—analogous to existing ISACs (Information Sharing and Analysis Centers) in sectors like finance and energy—should be created. These could coordinate threat intelligence, aggregate emerging patterns in AI abuse, and publish best practices across industries. As AI becomes increasingly embedded into national infrastructure, these partnerships will become essential to national security itself.
International coordination will also be critical. AI attacks are borderless, and so must be our defenses. Shared standards, joint response protocols, and bilateral agreements on AI research safety will all be necessary. No nation can afford to operate in isolation. The consequences of a large-scale AI failure—whether from cybercrime, misinformation, or compromised systems—will spill across borders quickly.
Final thoughts
AI represents a fundamental shift in the way we build, interact with, and depend upon technology. But with that transformation comes a responsibility to rethink everything we know about keeping systems safe. The threats posed by AI are not abstract—they are real, growing, and unlike anything the security community has faced before.
If we treat AI as just another kind of software, we will fail. We will miss critical risks, delay essential reforms, and remain blind to the ways these systems can be manipulated. But if we rise to the challenge—by creating new vulnerability frameworks, demanding transparency, securing the AI supply chain, and fostering a culture of security-first design—we can build systems that are not only powerful but trustworthy.
AI security is not a footnote. It is the foundation of the AI age. And the time to act is now.