The AI Triple Threat: Why Identity Security Must be the Cornerstone of AI Adoption
by David Higgins
AI brings new possibilities, but with it, new risks. This article looks at the three threats that AI brings and the best strategies to use identity security and keep cybersecurity at the forefront of digital strategies.
A series of recent high-profile breaches has demonstrated that the UK remains highly exposed to increasingly sophisticated cyber threats. This vulnerability is growing as artificial intelligence becomes more deeply embedded in day-to-day business operations. From driving innovation to enabling faster decision-making, AI is now integral to how organisations deliver value and stay competitive. Yet, its transformative potential comes with risks that too many organisations have yet to fully address.
CyberArk’s latest research shows that AI now presents a complex “triple threat”. It is being exploited as an attack vector, deployed as a defensive tool and, perhaps most concerning, introducing critical new security gaps. This dynamic threat landscape demands that organisations place identity security at the centre of any AI strategy if they wish to build resilience for the future.
AI is enhancing familiar threats
AI has raised the bar for traditional attack methods. Phishing, which remains the most common entry point for identity breaches, has evolved beyond poorly worded emails to sophisticated scams that use AI-generated deepfakes, cloned voices and authentic-looking messages. Nearly 70% of UK organisations fell victim to successful phishing attacks last year, with more than a third reporting multiple incidents. This shows that even robust training and technical safeguards can be circumvented when attackers use AI to mimic trusted contacts and exploit human psychology.
It is no longer enough to assume that conventional perimeter defences can stop such threats. Organisations must adapt by layering in stronger identity verification processes and building a culture where suspicious activity is flagged and investigated without hesitation.
AI as a defensive asset
While AI is strengthening attackers’ capabilities, it is also transforming how defenders operate. Nearly nine in ten UK organisations now use AI and large language models to monitor network behaviour, identify emerging threats and automate repetitive tasks that previously consumed hours of manual effort. In many security operations centres, AI has become an essential force multiplier that allows small teams to handle a vast and growing workload.
Almost half of organisations expect AI to be the biggest driver of cybersecurity spending in the coming year. This reflects a growing recognition that human analysts alone cannot keep up with the scale and speed of modern attacks. However, AI-powered defence must be deployed responsibly. Over-reliance without sufficient human oversight can lead to blind spots and false confidence. Security teams must ensure AI tools are trained on high-quality data, tested rigorously, and reviewed regularly to avoid drift or unexpected bias.
AI is expanding the attack surface
The third element of the triple threat is the rapid growth in machine identities and AI agents. As employees embrace new AI tools to boost productivity, the number of non-human accounts accessing critical data has surged, now outnumbering human users by a ratio of 100 to one. Many of these machine identities have elevated privileges but operate with minimal governance. Weak credentials, shared secrets and inconsistent lifecycle management create opportunities for attackers to compromise systems with little resistance.
Shadow AI is compounding this challenge. Research indicates that over a third of employees admit to using unauthorised AI applications, often to automate tasks or generate content quickly. While the productivity gains are real, the security consequences are significant. Unapproved tools can process confidential data without proper safeguards, leaving organisations exposed to data leaks, regulatory non-compliance and reputational damage.
Addressing this risk requires more than technical controls alone. Organisations should establish clear policies on acceptable AI use, educate staff on the risks of bypassing security, and provide approved, secure alternatives that meet business needs without creating hidden vulnerabilities.
Putting identity security at the centre
Securing AI-driven businesses demands that identity security be embedded into every layer of the organisation’s digital strategy. This means achieving real-time visibility of all identities, whether human, machine or AI agent, applying least privilege principles consistently, and continuously monitoring for abnormal access behaviours that may indicate compromise.
Forward-looking organisations are already adapting their identity and access management frameworks to handle the unique demands of AI. This includes adopting just-in-time access for machine identities, implementing privilege escalation monitoring and ensuring that all AI agents are treated with the same rigour as human accounts.
AI promises enormous value for organisations ready to embrace it responsibly. However, without strong identity security, that promise can quickly turn into a liability. The companies that succeed will be those that understand that building resilience is not optional, but foundational to long-term growth and innovation.
In an era where adversaries are equally empowered by AI, one principle holds true: securing AI begins and ends with securing identity.
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Managing Model Drift in LLMs for the Safe Use of AI
by João Freitas
Successfully implementing a successful LLMOps framework can help enterprises avoid that output from their LLMs stays free of model drift and AI hallucinations. This article explains how to create a successful LLMOps strategy, managing model drift, and ensure customer trust and satisfaction.
The number of business professionals using AI continues to grow as both sanctioned and unsanctioned use skyrocket, and organizations deploy commercially available LLMs internally. Given the increasing adoption of LLMs, organizations must ensure outputs from these models are trustworthy and repeatable over time. LLMs have become business-critical systems in modern enterprises, and any potential failure of these systems can rapidly harm customer trust, violate regulations and damage an organization’s reputation.
Foundational AI models are expensive to train and run, and in most business contexts, there is minimal return on investment for companies that invest millions in building their models. With this cost in mind, organizations instead choose to rely on LLMs developed by third parties, which must be managed in the same way other enterprise systems are managed.
However, organizations must be on guard for model drift and AI hallucinations when using these third-party models, and implement standardized processes to remediate these issues. This specialized space, called LLMOps, is emerging as organizations adopt dedicated platforms that extend traditional MLOps and observability frameworks to meet the unique challenges posed by widespread LLM use.
But what does a suitable LLMOps framework look like?
Forming the bedrock of LLMOps
It’s clear that organizations need LLMOps to mitigate the risk of hallucinations or model drift, but the practical aspects of an LLMOps framework can be less apparent. Several crucial considerations must form the bedrock of an organization’s LLMOps practices.
When any publicly available LLM is adopted by an organization, the first step in managing its use is to establish clear guardrails for the systems and data it can access. Approved use cases for the LLM must also be made clear across teams to strike the right balance between enabling innovation without ever exposing sensitive data or systems to a third-party provider, or crossing data permissions boundaries.
Similarly, organizations must set up a good level of observability around any LLMs to detect issues with latency or inaccurate outputs before they can escalate into issues that directly affect engineering teams. Both of these steps can improve organizational security around LLM usage to reduce the risk exposure often associated with the adoption of new tools.
To maintain the long-term accuracy and trustworthiness of LLM outputs, organizations must implement safeguards to reduce bias and ensure fairness in any outputs generated. LLMs are prone to bias, which is present in the data they were trained with. For example, LLMs often refer to developers as “he” rather than using a gender-neutral term. While this may seem innocuous, it can be a sign of other biases within the LLM, which can ultimately affect hiring decisions or internal company policies, often to the detriment of one or more groups.
It is also vital for organizations to test the LLMs they use for degradation over time due to changes in the data. This is necessary to ensure the model aligns with the data in their environment and provides an additional layer of security against AI hallucinations.
The final pillar of an effective LLMOps framework is for the organization to proactively address risks related to the generation of incorrect sensitive data, such as generating incorrect pricing. Sensitive, business-critical decisions cannot be wholly given over to LLMs. Instead, responsible LLMOps will keep human oversight for critical operations.
When successfully adopted, LLMOps will enable LLMs to scale as more users within an organization adopt tools with guardrails in place. LLMOps will also keep LLMs performing well so they never become blockers to innovation or cause operational slowdowns.
However, LLMOps is not a one-and-done process. Instead, LLMs must be constantly monitored and retained on up-to-date datasets to avoid model drift over time.
How LLMOps prevent model drift
With a vast number of organizations using commercially available LLMs, there is a growing risk of model drift influencing LLM-generated outputs as time goes on. The primary cause of model drift is a model basing its responses on outdated data. For example, an organization using GPT-1 would only receive answers based on that model’s training data, which comes from pre-2018, while GPT-4 has been trained on data up to 2023.
So, how can enterprises use LLMOps to combat model drift?
There are five strategies organizations can employ, depending on their datasets and computational resources:
- Use the latest version of an LLM model to account for more recent data, helping to ensure that any generated outputs will be up to date and reduce the chance of AI hallucinations where the LLM tries to fill gaps in its training data.
- Fine tune pre-trained LLMs to respond to a specific topic, improving the accuracy of outputs without the major investment of training a proprietary model.
- Adjust parameters for responses and adjust the weighting of responses to enable an LLM to give more importance to certain tokens over others in response generation.
- Use Retrieval-Augmented Generation (RAG) to enhance the LLM’s case-specific knowledge and factual accuracy by retrieving relevant information from external knowledge sources during inference.
- Pass sufficient, industry-focused context to the model to ensure users get better responses to questions and more relevant answers for the enterprise’s specific industry.
Successful LLMOps is continuous
While enterprises can adopt LLMOps to manage how teams use LLMs, they cannot treat it as a one-off process.
Preventing model drift requires constant supervision of AI-generated outputs and regular retraining of LLMs as an organization’s internal datasets evolve. Given the potentially damaging business impact of incorrect results, mitigating hallucination risk is crucial to the success of a modern organization.
Through the creation of an effective LLMOps strategy, organizations will be able to improve customer trust, ensure their regulatory compliance and protect their reputation, all while making their operations more efficient.
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FREQUENTLY ASKED QUESTIONS
What are the three main AI-related cybersecurity threats?
AI presents a triple threat: it serves as an attack vector, a defensive tool, and introduces new identity-related vulnerabilities. These roles increase the complexity and risk in enterprise cybersecurity strategies.
How has AI changed traditional phishing attacks?
AI has enabled highly convincing phishing scams using deepfakes, cloned voices, and realistic messages. These attacks bypass human training and technical safeguards, making identity verification critical.
How is AI used as a cybersecurity defense mechanism?
AI and LLMs help monitor networks, detect emerging threats, and automate repetitive security tasks. However, over-reliance without human oversight can result in blind spots and biased decisions.
What risks are introduced by machine identities in AI systems?
Machine identities now outnumber human users 100 to 1, often with high privileges and little governance. Poor credential management and lifecycle policies make them a major attack surface.
What is Shadow AI and why is it dangerous?
Shadow AI refers to employees using unauthorized AI tools without IT approval. This exposes sensitive data and creates compliance and reputational risks.
How can organizations secure AI-driven environments?
By embedding identity security into digital strategies: applying least privilege, monitoring access behavior, and managing both human and machine identities equally.
What are the foundational pillars of an LLMOps strategy?
LLMOps includes guardrails for LLM access, observability for latency and accuracy, bias reduction, data alignment, and human oversight for critical decisions.
What causes model drift in LLMs and how can it be mitigated?
Model drift results from outdated training data. It can be addressed through updated LLM versions, fine-tuning, RAG, parameter adjustments, and industry-specific prompts.
Why is continuous monitoring critical in LLMOps?
LLMOps must be an ongoing process. Regular retraining and supervision are required to ensure accuracy, prevent hallucinations, and uphold customer trust.
