Navigating the AI Frontier: A Security Blueprint

Author: Denis Avetisyan

Organizations face a rapidly evolving threat landscape as artificial intelligence becomes increasingly integrated into critical systems.

This report details a comprehensive framework for managing AI security and safety risks throughout the entire AI lifecycle, including a detailed threat taxonomy and risk management strategies.

While artificial intelligence offers unprecedented gains in productivity and innovation, its rapid deployment expands the attack surface across content, data, and runtime environments. The Cisco Integrated AI Security and Safety Framework Report addresses this growing challenge by presenting a unified, lifecycle-aware taxonomy for classifying and operationalizing the full range of AI risks. This framework integrates security and safety considerations across modalities, agents, and the broader AI ecosystem, offering a practical approach to threat identification and risk prioritization. As AI capabilities continue to advance, can organizations proactively build defenses that evolve alongside these emerging deployments in multimodal contexts and beyond?

The Expanding Threat Surface: A System’s Inevitable Exposure

The increasing prevalence of artificial intelligence within critical infrastructure – encompassing sectors like energy, finance, and transportation – dramatically expands the potential avenues for malicious actors. Traditionally, security efforts focused on safeguarding software code from exploitation; however, AI systems introduce entirely new vulnerabilities. These extend beyond code to include the data used to train the models, the models themselves as intellectual property, and the complex interactions between AI agents and the physical world. This broadened “attack surface” means a compromised sensor feeding data to an AI-controlled power grid, or a subtly manipulated training dataset influencing a financial algorithm, can have far-reaching and devastating consequences, demanding a fundamental shift in security paradigms to address these novel threats.

The increasing sophistication of artificial intelligence introduces vulnerabilities distinct from those in conventional software, demanding a shift in security paradigms. Notably, attacks like data poisoning – where malicious data is introduced during the training phase – can subtly corrupt an AI’s decision-making process, leading to unpredictable and potentially harmful outcomes. Simultaneously, model theft, involving the unauthorized extraction of an AI’s learned parameters, allows adversaries to replicate valuable intellectual property or even create competing systems with similar capabilities. These AI-specific threats aren’t simply variations of existing attacks; they exploit the unique characteristics of machine learning algorithms and necessitate the development of dedicated defense mechanisms, including robust data validation techniques, differential privacy methods, and advanced model watermarking strategies, to safeguard the integrity and confidentiality of these increasingly critical systems.

The increasing sophistication of artificial intelligence, specifically with large language models and agentic systems, introduces a new echelon of security challenges. These models, characterized by billions of parameters and emergent behaviors, defy traditional security paradigms built around predictable code execution. Unlike conventional software, vulnerabilities aren’t solely rooted in code flaws but also in the data used for training and the model’s inherent susceptibility to adversarial manipulation. The opacity of these ‘black box’ systems makes it difficult to anticipate potential failure modes or identify malicious inputs, while their ability to learn and adapt allows attackers to exploit unforeseen weaknesses. Consequently, conventional defenses – such as firewalls and intrusion detection systems – prove inadequate, necessitating the development of novel security measures focused on data integrity, model robustness, and runtime monitoring to safeguard these complex AI systems.

Foundations of Resilience: Managing Risk in a Complex System

A comprehensive AI Risk Management strategy necessitates continuous evaluation throughout the entire AI lifecycle. This begins with rigorous data acquisition processes, including validation for bias and adherence to privacy regulations. Risk assessment must then extend to model development, focusing on potential vulnerabilities and unintended consequences. Following model deployment, ongoing monitoring for performance drift, adversarial attacks, and ethical concerns is critical. This lifecycle approach ensures that risks are identified and mitigated at each stage, from initial data handling through ongoing operational use, and allows for iterative improvements to the AI system and its associated security posture.

The National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) is a structured process designed to improve the trustworthiness and responsible development and use of artificial intelligence. It functions as a guideline for organizations to manage risks associated with AI systems, encompassing four key functions: Govern, Map, Measure, and Manage. The “Govern” function establishes organizational context and risk tolerance; “Map” focuses on identifying the technical and operational characteristics of an AI system; “Measure” involves assessing the AI system’s performance against defined risk thresholds; and “Manage” details the implementation of controls to mitigate identified risks. The AI RMF is intended to be flexible and adaptable across various AI applications and organizational structures, and it supports compliance with evolving regulations and standards related to AI safety and security.

Responsible AI principles directly contribute to system security by minimizing vulnerabilities arising from biased outputs, unpredictable behavior, and lack of auditability. Fairness in AI systems reduces the risk of discriminatory outcomes that could be exploited or lead to legal challenges, while transparency-encompassing explainability and interpretability-allows for thorough examination of model logic and identification of potential security flaws. Accountability mechanisms, including clear ownership of AI systems and documented decision-making processes, enable effective incident response and mitigation of harm resulting from system failures or malicious attacks. Implementing these principles throughout the AI lifecycle is therefore crucial for building systems that are not only reliable and ethical, but also demonstrably secure and trustworthy.

Mapping the Battleground: Understanding Attack Vectors in the Wild

The MITRE ATLAS knowledge base details a growing number of adversarial tactics used to compromise AI systems. This resource catalogs techniques such as prompt injection, where malicious input is crafted to manipulate the behavior of large language models, and data poisoning, which involves injecting flawed or biased data into the training dataset to degrade model performance or introduce backdoors. ATLAS categorizes these attacks based on the ATT&CK framework, providing a standardized method for documenting and mitigating AI-specific threats. The database includes detailed information on each technique, including examples, detection strategies, and potential mitigations, allowing security professionals to understand and address vulnerabilities across various AI applications and model types. It is regularly updated to reflect emerging threats and attack vectors in the rapidly evolving landscape of artificial intelligence security.

The Open Web Application Security Project (OWASP) offers application-specific security guidance for artificial intelligence systems, notably through the OWASP Top 10 for Large Language Model (LLM) and Agentic Applications. This resource details ten critical security risks inherent in LLM and agent-based deployments, including prompt injection, insecure output handling, and denial of service. The OWASP Top 10 provides a prioritized list of vulnerabilities, detailing attack vectors, likelihood, and impact, alongside mitigation strategies tailored to these emerging technologies. It focuses on vulnerabilities stemming from the unique characteristics of LLMs and agents, such as their reliance on natural language processing and autonomous actions, providing practical recommendations for developers and security professionals.

NIST AI 100-2, formally titled “Adversarial Machine Learning Taxonomy,” categorizes attacks based on the attacker’s knowledge, goal, and technique. The taxonomy defines three primary attack surfaces: evasion attacks which attempt to cause misclassification at inference time, poisoning attacks targeting the training data to degrade model performance, and backdoor attacks inserting hidden triggers into the model. Attackers are further classified by their knowledge – ranging from ‘black-box’ with no model information to ‘white-box’ with full access. The document details specific mechanisms within these categories, such as feature space attacks, adversarial examples generated via gradient-based methods, and data manipulation techniques used in poisoning scenarios, providing a structured framework for understanding and mitigating adversarial threats to machine learning systems.

Building Robust Defenses: A Framework for System Resilience

Google’s Secure AI Framework (SAIF) advocates for a comprehensive risk management strategy applied across all phases of the AI lifecycle – from model design and data sourcing to deployment and monitoring. This holistic approach prioritizes proactive security measures, including threat modeling, vulnerability analysis, and the implementation of robust defenses against adversarial attacks and data breaches. SAIF emphasizes the importance of integrating security considerations into the initial stages of AI development, rather than treating them as an afterthought, and promotes continuous monitoring and adaptation to address emerging threats and vulnerabilities throughout the system’s operational lifespan. The framework aims to facilitate the development of AI systems that are not only functional and performant but also demonstrably secure and trustworthy.

Constitutional AI, pioneered by Anthropic, utilizes a reinforcement learning from human feedback (RLHF) approach that differs from traditional methods by replacing direct human labeling of desired outputs with a set of principles, or a “constitution.” During training, the model is prompted to self-evaluate its responses against these principles and revise them accordingly, minimizing reliance on potentially biased or inconsistent human feedback. This process involves two stages: initially, the model is trained to identify responses that violate the constitution, and subsequently, it’s trained to revise those responses to align with the specified principles. The resulting system demonstrates improved safety and reduced harmful outputs, as the model learns to internalize and apply the safety constraints during the generation process, offering a scalable and potentially more objective approach to AI safety.

The presented AI security and safety framework utilizes a hierarchical structure to systematically address risks in contemporary AI systems. It defines 19 overarching objectives, encompassing areas such as data security, model robustness, and responsible AI deployment. These objectives are then broken down into 40 specific techniques, detailing concrete methods for mitigation. Further granularity is achieved through 112 subtechniques, providing detailed implementation guidance. This categorization allows for a structured assessment of potential vulnerabilities and the application of targeted countermeasures, ultimately aiming to enhance the trustworthiness and reliability of AI applications within an evolving threat landscape.

The Inevitable Cycle: Adapting to a Shifting Threat Landscape

The dynamic nature of artificial intelligence necessitates a security posture centered on perpetual vigilance and responsive adaptation. Adversarial techniques, designed to exploit vulnerabilities in AI systems, are not static; instead, they continuously evolve as defenses improve, creating an ongoing arms race. Consequently, a ‘set it and forget it’ approach to AI security is demonstrably ineffective; systems require constant monitoring to detect novel attacks and proactive adaptation to incorporate newly developed countermeasures. This continuous cycle of observation, analysis, and refinement is paramount to maintaining the integrity and reliability of AI, ensuring that defenses remain effective against increasingly sophisticated threats and preserving trust in these powerful technologies.

The escalating sophistication of adversarial attacks against artificial intelligence necessitates a concerted effort beyond individual research silos. Effective AI security relies heavily on the open exchange of threat intelligence, vulnerability analyses, and mitigation strategies within the AI community. This collaborative approach allows for the rapid identification of emerging threats and the development of proactive defenses, preventing isolated incidents from becoming widespread vulnerabilities. Sharing datasets of adversarial examples, best practices for robust model training, and insights into attack vectors accelerates innovation and builds a collective resilience. Furthermore, standardized reporting frameworks and coordinated vulnerability disclosure programs are crucial for fostering trust and enabling a swift response to newly discovered weaknesses, ultimately safeguarding the integrity and reliability of AI systems.

Future AI system robustness hinges on proactive research into advanced security techniques. Investments in areas like explainable AI – allowing humans to understand the reasoning behind AI decisions – and differential privacy – protecting sensitive data while still enabling analysis – are paramount. A recently developed framework further accelerates this progress by meticulously identifying 25 distinct categories of harmful content, ranging from hate speech to malicious code. This detailed categorization doesn’t just highlight current vulnerabilities; it provides a concrete roadmap for focused research and the development of targeted mitigation strategies, ultimately fostering AI systems that are not only intelligent but also demonstrably trustworthy and resilient against evolving threats.

The pursuit of absolute security in any complex system, particularly those involving agentic AI, is fundamentally misaligned with reality. This framework, detailing a threat taxonomy and risk management strategies, doesn’t prevent failures – it prepares for them. As Claude Shannon observed, “The most important thing in communication is to convey the meaning, not the message.” Similarly, in AI safety, the goal isn’t to eliminate risk, but to understand and navigate it. Stability, as an illusion that caches well, is a temporary state, and the framework acknowledges the inevitable entropy inherent in evolving AI systems. It’s not a shield, but a constantly adapting lens through which to view an unpredictable landscape.

The Looming Shadows

This framework, meticulously detailing the anticipated failures of agentic systems, resembles less a fortification than a detailed map of the inevitable breaches. Each categorized threat, each lifecycle stage scrutinized, is a testament not to control, but to the persistent asymmetry between intention and outcome. The taxonomy itself will prove transient; the landscape of adversarial tactics shifts with every released model, every novel prompting technique. It charts a territory destined to be overgrown.

The focus on supply chain security, while pragmatic, merely pushes the point of failure further upstream. Trust, after all, is a localized illusion. The real challenge isn’t securing components, but acknowledging that every dependency introduces a vector for subtle, emergent misbehavior. To believe a perfectly secured chain is achievable is to deny the fundamental law of increasing disorder.

Future iterations will inevitably expand the threat model, layering complexity upon complexity. But the core problem remains: systems aren’t built, they grow. And growth, by its very nature, is unpredictable. The true metric of success won’t be the number of threats identified, but the organization’s capacity to absorb the failures that were, despite all efforts, statistically guaranteed.

Original article: https://arxiv.org/pdf/2512.12921.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/