The security of applications based on large language models (LLMs) is an increasingly relevant topic as the integration of these technologies into business systems and public services becomes more widespread. The new 2025 version of the OWASP Top 10 LLM list for vulnerabilities in LLM-based applications describes the most critical risks these applications face. This article is a summary of the work conducted by the OWASP team and involved institutions, based on contributions from security experts, developers, and data scientists from various sectors. In this article, we will explore each of the identified vulnerabilities, providing concrete examples and possible mitigation strategies.
Prompt Injection (OWASP Top 10 LLM)
The issue of Prompt Injection occurs when a user's input manages to manipulate the behavior of a language model, altering the model's output or actions in unintended ways. This vulnerability can be exploited both intentionally, by malicious actors who provide input designed to deceive the model, and accidentally, when unexpected inputs lead to incorrect system behavior. A particularly complex aspect is that prompt injection attacks may not be visible or readable by humans: any content that the model can interpret can potentially influence its behavior.
There are two main types of Prompt Injection attacks: direct and indirect. Direct attacks occur when an attacker directly introduces a command or input that induces the model to perform unwanted actions, such as ignoring security guidelines, revealing sensitive information, or even performing dangerous actions like accessing unauthorized resources. Indirect attacks, on the other hand, occur through input from external sources, such as files or websites, which contain instructions that the model can interpret and that alter its behavior.
A new emerging challenge is related to multimodal models, which are designed to handle different types of input, such as text and images simultaneously. Attackers could, for instance, hide instructions within images accompanying text. These cross-modal attacks significantly increase the attack surface, making defending against prompt injection far more complex.
The impact of a prompt injection attack can be devastating: from disclosing sensitive information to bypassing system security measures and manipulating the model's critical decisions. For example, an attacker could use hidden prompts to make a customer service chatbot ignore all internal security rules, allowing access to confidential personal data.
To mitigate the risk of prompt injection, it is essential to adopt multiple protection strategies. First of all, limiting the model's behavior by precisely defining its roles and capabilities is a crucial step. Providing clear instructions to the model on what is and is not allowed helps prevent unwanted deviations. Additionally, it is important to filter inputs and outputs using semantic tools that can identify potentially harmful content. For instance, implementing input validation controls and content filtering rules can help reduce the risk of malicious inputs.
The adoption of an approach called "human-in-the-loop" can also contribute to security. This approach requires that some high-risk actions need human operator confirmation before being executed, thus limiting the possibility that a malicious prompt leads to severe consequences. Furthermore, segregating external content and clearly identifying which data comes from untrusted sources further reduces the potential impact of a prompt injection attack.
Finally, testing the model regularly through attack simulations and penetration testing techniques can help identify security flaws before they are exploited. These tests should treat the model as an untrusted user to evaluate the effectiveness of trust boundaries and access controls.
Sensitive Information Disclosure (OWASP Top 10 LLM)
The vulnerability of Sensitive Information Disclosure occurs when a language model handles personal or confidential data without adequate security controls. This issue can have serious consequences, especially when information is inadvertently disclosed during interaction with the model or due to poor data management practices during training. The nature of these models, trained on vast amounts of data, can lead to situations where private details are unexpectedly revealed if the data has not been properly filtered.
One of the most common cases of sensitive information disclosure involves the leakage of personally identifiable information (PII), such as names, addresses, phone numbers, and other sensitive details. For example, in contexts where an LLM is used for customer support, it could inadvertently reveal personal data of another user if proper access controls are not in place. This situation can occur when the model has been trained using data that is not fully anonymized or when information is stored without adequate protection measures.
Another significant risk is the exposure of proprietary algorithms or internal details of an organization. For example, a model used to solve business problems could accidentally reveal confidential information about proprietary algorithms or methodologies, exposing the company to potential security risks and loss of competitive advantage. This type of disclosure can occur not only due to errors in managing outputs but also because of targeted attacks exploiting vulnerabilities in prompts or training data.
To mitigate these risks, it is crucial to adopt data sanitization techniques during the training process, ensuring that any personal or sensitive data is removed or masked. Sanitization must be performed not only on the data used for training but also on real-time user inputs. Additionally, the adoption of federated learning techniques can reduce the need to transfer sensitive data to a single centralized location, thereby decreasing the risk of exposure.
The implementation of access controls based on the principle of least privilege is another key measure to prevent sensitive information disclosure. This approach implies that the model only has access to the information strictly necessary to perform its task, thus limiting the possibility that confidential information is processed or disclosed by mistake. Another useful technique is the use of differential privacy, which adds "noise" to the data to ensure that specific user information cannot be reconstructed from the results generated by the model.
Educating users about the safe use of LLMs is equally important. Users must be aware of the risks associated with entering sensitive data and should receive guidelines on how to interact with the model safely. For example, the service's terms of use should clarify that the entered data might be used to improve the model, and users should be given the option to opt out of having their data used for training.
Finally, it is essential to properly configure the system to avoid having confidential information included in system prompts or outputs generated by the model. Infrastructure security must be ensured by following best practices, such as those defined by OWASP, including the secure configuration of APIs and masking error messages to prevent leaks of critical information.
Supply Chain Vulnerabilities (OWASP Top 10 LLM)
Supply chain vulnerabilities in LLM-based applications represent a significant risk, as they can compromise the integrity of the models, training data, and deployment platforms. These vulnerabilities can arise from various external elements, such as pre-trained models or third-party software components. Using publicly available pre-trained models, for example, carries an inherent risk because such models may contain biases or even malicious backdoors, introducing weaknesses that are difficult to detect.
A critical aspect is the use of outdated models that are no longer updated or maintained. The adoption of unsupported models or software components represents a common security flaw, similar to those described in other areas of cybersecurity (such as managing outdated software), but with potentially much greater impact, given the pervasive use of LLMs in critical contexts. If a model is not updated, discovered vulnerabilities can be exploited by malicious actors, leading to possible data breaches or system attacks.
Another risk concerns fine-tuning methods based on techniques such as Low-Rank Adaptation (LoRA). While these techniques allow for more efficient adaptability and performance improvements, they also introduce new risks. An attacker could exploit vulnerabilities in these adaptations to compromise their integrity, manipulating the base model at the component level and inserting unwanted behaviors. For example, a malicious LoRA adapter could be loaded from an unverified source, compromising the entire system.
Moreover, collaborative development and model merging processes, such as those widely adopted on platforms like Hugging Face, represent a notable attack surface. Model sharing platforms are often vulnerable to compromises due to misconfiguration or inadequate security controls. Model tampering attacks could include directly modifying a model's parameters to insert backdoors or biases that are not detectable during common usage.
To mitigate these risks, it is crucial to maintain an accurate and updated inventory of all components used in the supply chain, utilizing tools like the Software Bill of Materials (SBOM), which allows verification of the origin and security of software components and pre-trained models. This enables the rapid identification of any known vulnerabilities and the evaluation of the system's overall security.
The implementation of AI Red Teaming practices, involving specialized teams simulating attacks to identify vulnerabilities, can be highly effective in testing the resilience of models and components against real threats. It is equally important to continuously monitor and verify the security of collaborative development environments by introducing auditing mechanisms that allow the timely detection of anomalies or abuses.
Finally, creating a constant update and patching policy for components used in models is crucial to ensure that any vulnerability is resolved as quickly as possible, thereby limiting the risk of exposure to potential exploits. The use of model encryption techniques, especially for models distributed on local devices, and the integration of integrity checks can prevent model tampering and limit unauthorized access.
Data and Model Poisoning (OWASP Top 10 LLM)
Data and Model Poisoning occurs when the data used to train the model is manipulated to introduce vulnerabilities, biases, or even to deliberately compromise the model. This type of attack can negatively affect the model's performance, leading to incorrect decisions or unexpected behaviors. One of the main risks is that training data, especially data from external sources, may contain malicious information that alters the model's ability to make accurate predictions. This is particularly true when models are trained on unverified datasets or data collected from public environments, where attackers can easily inject adversarial content.
For instance, an attacker could manipulate the dataset by inserting specific examples designed to teach the model to behave incorrectly in certain situations. This type of attack, known as backdoor insertion, can leave the model seemingly normal until a specific trigger alters its behavior. Such an attack could allow the attacker to bypass security measures or directly manipulate the model's responses.
To mitigate these risks, it is crucial to implement data traceability measures. Using tools like the Machine Learning Bill of Materials (ML-BOM) helps track the origin and transformations of data throughout the model's lifecycle. Data validation is equally important: every piece of data should undergo a rigorous verification process before being used for training, especially if it comes from external or collaborative sources.
Another effective strategy is using data version control (DVC) to monitor every change in datasets. This helps detect any data manipulation and maintain the integrity of the entire model development process. Additionally, the implementation of adversarial learning techniques helps prepare the model to withstand attacks, improving its robustness against malicious perturbations.
Another step to prevent model poisoning involves adopting sandboxing to limit the model's exposure to unverified data. Creating an isolated environment in which to test new data before actually using it for training reduces the risk of compromising the model. Finally, using monitoring and anomaly detection techniques during the training process helps identify unexpected behaviors in the model that could indicate the presence of poisoned data.
Improper Output Handling (OWASP Top 10 LLM)
Improper handling of outputs generated by LLM models can expose applications to a wide range of vulnerabilities, including remote code execution (RCE), cross-site scripting (XSS), and SQL injection attacks. This problem occurs when the output produced by the model is used without adequate validation or sanitization. Since LLMs are systems that generate text based on potentially unverified inputs, they can be exploited to introduce malicious commands that are then executed by subsequent components of the application chain.
For example, model output that is fed into a shell system without being verified could allow an attacker to execute arbitrary commands, compromising the entire system. Similarly, SQL queries generated by the LLM and used to access databases without proper parameterization could lead to SQL injection vulnerabilities, allowing unauthorized access to data. In web contexts, unsanitized output displayed in a browser could result in cross-site scripting (XSS) attacks, where the attacker introduces malicious scripts that are executed by the user's browser.
To mitigate these risks, it is crucial to treat every output generated by the model as potentially dangerous, applying strict validation and sanitization practices. The adoption of context controls, such as encoding the output based on the target environment (HTML, SQL, JavaScript), is an essential measure to ensure that generated content cannot be used maliciously. Using parameterized queries for all database operations reduces the risk that unverified inputs could alter the intended operations. Moreover, implementing a Content Security Policy (CSP) can limit the impact of XSS attacks by preventing unauthorized scripts from executing.
The use of advanced logging and monitoring systems can help detect abnormal behaviors in the outputs generated by the models. For example, constantly monitoring the content generated by the LLM and identifying suspicious patterns can provide an additional level of security, enabling rapid intervention in case of malicious activity detection. It is also important to define rate limits and usage quotas to prevent abuse, especially in contexts where the model has access to critical functions or sensitive resources.
Ultimately, ensuring proper output handling means adopting a "zero trust" approach towards generated content, treating the model as a possible attack vector, and implementing all necessary safeguards to protect downstream systems from potential compromises.
Excessive Agency (OWASP Top 10 LLM)
The concept of Excessive Agency refers to the excessive autonomy granted to a large language model (LLM), which can lead the model to take critical actions without adequate human supervision. LLMs with excessive autonomy can make decisions or perform operations that are outside their intended scope, potentially causing harm or security breaches. This risk becomes more critical with the growing spread of agent-based architectures, where an LLM is used as a decision point to perform various actions.
In the context of an LLM application, autonomy can include the model's ability to invoke system functions, access external resources, or communicate with other parts of a system without human confirmation. This capability can be useful for automating tasks, but at the same time, it introduces vulnerabilities when action controls are not sufficiently limited.
A common example of excessive autonomy concerns an LLM used as an assistant for email management, which might have access not only to read emails but also to send and delete them. This type of access exposes the system to significant risk, especially if an attacker manages to manipulate the LLM through malicious prompts or compromised external data. If the model is not designed to require human confirmation before performing certain operations, an attack could result in unauthorized emails being sent or critical information being deleted.
Another example can be represented by the use of unnecessary plugins or extensions that increase the range of functionalities available to an LLM. If a model is enabled to interact with a file management system, and this extension allows both reading and modifying files, the risk is that unwanted behavior or a targeted attack could lead to the modification or deletion of sensitive data. Plugins with extended functionalities that are not strictly necessary for the intended operation represent a risk vector because they offer additional access points that can be exploited.
A related issue is excessive permissions. Very often, LLMs are configured to operate with excessive privileges, allowing them to access functionalities or resources that are not essential for their operations. For example, an extension that only needs to read data from a database might be configured with write, modify, or delete permissions, creating a broader attack surface. Such misconfiguration makes the system vulnerable not only to possible attacks but also to errors that may result from the model's unexpected behavior.
To mitigate the risk of excessive autonomy, it is essential to adopt an approach that minimizes the extensions and functionalities available to the LLM. Extensions should be limited to only strictly necessary operations, thus reducing the model's ability to perform harmful actions. It is crucial to apply the principle of least privilege, ensuring that each extension or plugin operates with the lowest possible privileges, required only for the specific intended operation. In this way, even if the model is compromised, the actions it could perform would be severely limited.
Moreover, the implementation of human-in-the-loop mechanisms is crucial to ensure that all high-impact actions require confirmation from a human operator before being executed. For example, if an LLM is used to generate content to be published on social media, the final publication should always be manually approved by a human operator to avoid errors or abuse.
Finally, it is important to implement continuous monitoring of the model's activities, logging all operations performed and identifying any abnormal behaviors. This type of logging can help quickly detect suspicious activities and respond effectively. Additionally, adopting rate limits and restrictions on the number of actions an LLM can perform within a given time frame helps prevent abuse and limit the impact of possible compromises.
The risk of Excessive Agency is therefore closely linked to the management of the capabilities and permissions granted to LLMs. A well-designed architecture, which adopts mitigation measures such as the principle of least privilege, human supervision for critical actions, and continuous monitoring of activities, can significantly reduce exposure to this type of vulnerability, ensuring that the LLM always operates within safe and controlled limits.
System Prompt Leakage (OWASP Top 10 LLM)
The vulnerability of System Prompt Leakage involves the risk that system prompts, which are the instructions used to guide the model's behavior, may contain sensitive information that is not intended to be disclosed. System prompts are designed to provide the model with the directives needed to generate appropriate outputs, but they might inadvertently include confidential or critical data. When this information is uncovered, it can be used to facilitate other types of attacks, thus posing a significant risk to system security.
A common example of System Prompt Leakage occurs when prompts contain access credentials, API keys, or configuration details that should remain secret. If an attacker manages to extract these prompts, they can exploit them for unauthorized access to system resources, with potentially severe consequences. A specific case reported in the OWASP 2025 research shows how, in various business environments, information such as the structure of internal permissions or user financial transaction limits has been inadvertently exposed, thereby increasing the risk of privilege escalation attacks or bypassing security limits.
Moreover, System Prompt Leakage vulnerability can reveal internal filtering criteria used to prevent the model from providing sensitive responses. For example, a system prompt might contain instructions like: “If a user requests information about another user, always respond with ‘Sorry, I cannot assist with this request.’” If an attacker were to see this prompt, they could exploit it to bypass security measures and manipulate the model's behavior in unintended ways.
To mitigate the risk of System Prompt Leakage, it is crucial to separate sensitive data from system prompts and avoid including any critical information directly in them. Sensitive information should be managed through systems external to the model, ensuring that the model does not have direct access to such data. Another effective approach is to implement external guardrails: while training for specific behaviors can be useful, it does not guarantee that the model will always follow the instructions, especially in attack situations. An independent system that checks outputs to ensure compliance with expectations is preferable to relying solely on system prompt instructions.
A critical mitigation strategy is to ensure that security controls are applied independently of the LLM. This means that essential controls, such as privilege separation and authorization verification, must be performed in a deterministic and verifiable manner, and should never be delegated to the model. For instance, if an LLM agent performs tasks requiring different levels of access, multiple agents should be used, each configured with the minimal privileges needed to perform its task, thereby reducing the risk of accidental exposure of sensitive data.
In summary, the risk associated with System Prompt Leakage is not simply about disclosing the prompts themselves, but rather about the presence of sensitive data or excessive authorizations within them. Implementing robust external controls and limiting prompt content to non-sensitive information are essential steps to protect the integrity and security of LLM-based applications.
Vector and Embedding Weaknesses (OWASP Top 10 LLM)
Weaknesses in embeddings and vectors represent another significant security risk for LLMs. Embeddings are numerical representations that capture the meaning of text and are fundamental to the functioning of LLMs. However, these representations can be exploited to manipulate the model or extract sensitive information, especially if they are not protected by adequate security controls.
One of the primary vulnerabilities is embedding inversion, a type of attack in which an attacker uses embedding vectors to reconstruct sensitive information originally included in the training data. This inversion process can reveal private user details or proprietary data used to train the model, thereby compromising privacy. A concrete example reported in the OWASP 2025 research illustrates how an attacker managed to recover personal information, such as names or addresses, by analyzing embedding vectors generated by an inadequately protected LLM.
Additionally, embeddings can become vulnerable due to insufficient access controls. In systems using Retrieval-Augmented Generation (RAG) techniques, information contained in vectors can be retrieved and combined with new queries, creating the risk of sensitive data leakage between different users or usage contexts. For example, in multi-tenant environments, an error in the logical separation of requests could cause one user to receive information related to another user, leading to a confidentiality issue.
To mitigate these risks, it is essential to implement granular access controls that limit the use of embeddings to secure and verified contexts. Embeddings should be managed so that access is tightly controlled and authorized only for specific purposes. Additionally, techniques such as encrypting data within embeddings can help prevent the risk of inversion and information leakage. It is equally important to establish strict data validation policies to ensure that the information used to create embeddings is clean and comes from reliable sources.
Another step toward mitigation involves continuously monitoring the use of embeddings and RAG resources, maintaining a detailed log of access activities. This allows for the timely detection of abnormal behavior that might indicate manipulation attempts or unauthorized access. Monitoring can be combined with anomaly detection techniques to quickly identify possible attacks and mitigate their impact.
In summary, weaknesses in embeddings and vectors pose a significant challenge for LLM security. Implementing strict access controls, encrypting data, and constantly monitoring activity are all critical measures to protect these elements and ensure the security and confidentiality of LLM-based applications.
Misinformation (OWASP Top 10 LLM)
Misinformation represents one of the most critical risks in the use of LLMs, as the models can generate content that appears accurate but is completely incorrect or misleading. This risk is amplified by the ability of LLMs to produce responses that sound credible but are based on erroneous data or misinterpretations. Misinformation can lead to security violations, reputational damage, and even legal consequences, especially in contexts where the reliability of information is crucial, such as healthcare, finance, or law.
One of the main issues underlying misinformation is the phenomenon of hallucinations, where the model "invents" answers when there is a lack of concrete data. When the LLM does not have precise information on a particular subject, it may fill in the gaps with statistically generated data that seem accurate but are actually fabricated. For example, in the OWASP 2025 research, there have been documented cases where LLMs provided nonexistent legal references or health details with no scientific basis. This type of misinformation can lead users to make poor decisions, with potentially harmful consequences.
Another related problem is the excessive trust users may place in content generated by LLMs. Since responses often appear very confident and detailed, users tend not to verify their accuracy, integrating incorrect information into decision-making processes without proper checks. This can be particularly risky in sensitive contexts. For instance, a medical chatbot providing incorrect information could harm a patient's health, and a model used in the financial sector could lead to disastrous economic decisions.
To reduce the risk of misinformation, an effective strategy is to use Retrieval-Augmented Generation (RAG), which allows the model to access updated and verified sources of information during response generation. This approach reduces the risk of hallucinations, as responses are based on concrete data rather than statistical generation. Moreover, it is important to integrate human supervision into decision-making processes, especially in critical fields: manually verifying the information generated by the model can improve overall accuracy and reduce the spread of erroneous content.
Another mitigation technique is model refinement through fine-tuning and using embeddings that improve response quality. Techniques like parameter-efficient tuning (PET) and chain-of-thought prompting can significantly reduce the incidence of misinformation, as they enable the model to perform more structured reasoning and verify the consistency of generated information.
Finally, it is crucial to educate users on the limitations of LLMs and the importance of independent verification of generated content. Providing specific training to users, especially in sensitive contexts, helps avoid excessive reliance on model-generated content and develop a more critical approach to using these technologies.
In conclusion, misinformation represents a central vulnerability for LLMs but can be mitigated through a multidimensional approach combining the use of external sources, human supervision, continuous model refinement, and user education. Only through rigorous control and constant verification is it possible to minimize the risks associated with the dissemination of incorrect information by these models.
Unbounded Consumption (OWASP Top 10 LLM)
Unbounded Consumption refers to the risk of an LLM using computational resources in an uncontrolled manner, with potential consequences of denial of service or high operational costs. LLMs, especially those hosted in cloud environments with "pay-per-use" billing models, can be vulnerable to excessive and unauthorized use, leading to unsustainable costs for the managing organization.
A common example of this risk is the so-called Denial of Wallet (DoW), where an attacker exploits the pay-per-use system to generate continuous and costly requests to the model, causing a significant increase in service costs. This type of attack not only can economically harm the organization but can also have operational consequences, limiting service availability for legitimate users. In the 2025 research, specific cases have been reported where a company's operational cost grew exponentially due to a DoW attack, highlighting how this can represent a significant financial threat.
Another typical situation of Unbounded Consumption occurs when users repeatedly submit very complex inputs or long sequences, causing disproportionate use of the model's resources. In these cases, the system can become slow or even stop responding due to excessive computational pressure. An example might be the use of linguistically intricate requests that require significant processing, resulting in inefficient use of CPU and memory.
To mitigate these risks, it is crucial to implement rate limits and usage quotas that regulate the maximum number of requests a single user can make within a given time period. This helps prevent resource abuse and ensures a fair distribution of computational capacity among users. The OWASP research emphasizes the importance of limiting the exposure of logits and other sensitive information during API interactions, thereby reducing potential attack vectors for exploiting the model.
Another effective approach is continuous resource monitoring, which allows for detecting abnormal usage and quickly responding to suspicious behavior. Alarm systems and rate limiting can be configured to automatically intervene when model usage exceeds certain thresholds, ensuring that resources always remain within manageable limits.
Finally, it is useful to consider implementing controlled system degradation techniques. Under excessive loads, the system can be designed to maintain partial functionality rather than undergo a complete shutdown. This ensures that at least some services remain operational even during significant attacks or overloads, thereby reducing the negative impact on the end-user experience.
These multidimensional approaches are fundamental to addressing the risk of Unbounded Consumption in LLM applications and ensuring service continuity, economic sustainability, and operational security of implementations based on these models.
Conclusions
The growing integration of large language models (LLMs) into business processes and public services has led to increased attention to their security, highlighting the need to address new vulnerabilities. These risks, while technical, have profound strategic implications for businesses, particularly in terms of trust, reputation, compliance, and economic sustainability. Understanding the vulnerabilities identified in the OWASP Top 10 LLM 2025 report enables the development of unique perspectives and exploration of innovative strategies to mitigate risks while maximizing the value derived from these advanced technologies.
A key takeaway is that vulnerabilities are not limited to the technology itself but often arise from the interaction between models, data, and business processes. For instance, the issue of “Prompt Injection” is not just a technical challenge but calls into question the reliability of the model as a decision-making tool. When a model can be manipulated through malicious inputs, companies must rethink their trust in the generated outcomes and build more resilient ecosystems. Adopting approaches like “human-in-the-loop” is not only a security measure but becomes a strategic choice to balance automation and human control, preserving decision quality in critical scenarios.
The “Disclosure of Sensitive Information” instead highlights how fragile the boundary between technological innovation and privacy protection is. Companies can no longer consider data security as a separate technical requirement but must integrate it into their governance strategies. This implies building systems that go beyond simple anonymization, embracing concepts such as differential privacy and federated learning. Such approaches not only reduce risks but offer a competitive advantage in a context where consumer trust is a strategic asset.
Vulnerabilities in the supply chain highlight how AI security depends on complex networks of suppliers and partners. Relying on pre-trained models or third-party components introduces systemic risks that require proactive management. Companies must start considering the security of model supply chains as an integral part of their risk management strategy, adopting tools like the Software Bill of Materials (SBOM) to ensure transparency and control.
“Misinformation” represents a vulnerability with broader strategic consequences, as it undermines not only the credibility of the technology but also that of the businesses that use it. Companies must address this challenge by embracing a model of accountability to end users. This means not only implementing verification and oversight systems but also educating the public to understand the technology's limitations. Such awareness can transform a reputational risk into an opportunity to strengthen trust.
Finally, the risk of “Unbounded Consumption” emphasizes that adopting LLMs is not only about technological innovation but also about economic sustainability. Inefficient resource management can quickly turn into a financial problem, making it essential for companies to implement monitoring and control mechanisms. Furthermore, the concept of “denial of wallet” introduces a new perspective on AI costs, pushing organizations to consider architectural solutions that balance performance and protection.
Companies wishing to harness the potential of LLMs must adopt an integrated vision that goes beyond technical security, embracing a strategic approach that considers trust, governance, resilience, and economic sustainability. This requires rethinking the entire implementation lifecycle, from design to operational management, to build systems that are not only secure but also aligned with business objectives and capable of responding to future challenges.
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