AI Security Trends to Watch in 2025

Executive Summary

Artificial Intelligence is fundamentally reshaping the technological landscape, introducing unprecedented opportunities alongside complex security challenges.

This blog highlights key AI security trends that will define 2025:

1. The emergence of autonomous AI agents capable of independent decision-making and task execution
2. The rise of collaborative multi-agent systems that work in coordinated teams
3. The proliferation of smaller, more accessible AI models
4. Advanced computer operation capabilities by AI systems
5. The implementation of comprehensive AI governance frameworks
6. The expansion of multi-modal AI processing capabilities

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Top AI Security Trends for 2025

The AI landscape of 2025 stands at a critical inflection point. According to IEEE's latest global technology survey, AI dominates as the most important technology, with 58% of technology leaders predicting it will be the most significant area of tech in the year ahead. This overwhelming consensus reflects AI's unprecedented impact across industries, supported by projections of global AI spending reaching $200 billion by 2025.

However, this rapid advancement brings complex challenges. Organizations face a dual imperative: leveraging AI's transformative potential while ensuring robust security measures against evolving threats. The survey reveals that cybersecurity is top-of-mind for technology leaders, with 48% identifying real-time vulnerability detection and attack prevention as the primary use case for AI in 2025.

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This blog explores six key security trends that will shape the AI landscape in 2025. Understanding these trends is crucial for organizations and individuals alike, as they will fundamentally impact how we approach AI security, compliance, and risk management in our increasingly AI-driven world.

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1. From Chatbots to Copilots and AI Agents

AI tools are evolving fast. What started as simple chatbots with basic capabilities has now advanced to “copilots” that assist with more complex tasks, such as understanding and summarizing multiple documents, and ultimately to fully autonomous “AI agents.” These new AI agents, sometimes called “agentic AI,” are built to act independently to complete tasks and make decisions without constant human input.

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Agentic AI combines multiple AI systems, allowing agents to plan, learn, sense their environment, and use tools to carry out different tasks. They can perform various tasks autonomously to meet specific goals and improve productivity. Gartner predicts that in 2028, these autonomous AI agents will make 15% of daily work decisions, up from 0% in 2024.

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However, agentic AI brings with it new security challenges:

• The more tasks AI agents handle, the more system entry points they create for potential attacks.
• With access to sensitive data, AI agents could be vulnerable to data leaks or unauthorized access.

With these new challenges, we might also see the introduction of security frameworks specifically designed for agents. These frameworks will cater to the challenges mentioned above, such as streamlining security protocols related to all modules the agent will access.

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2. Multi-Agent System

As AI continues to evolve, the idea of a single AI agent is expanding into something much larger and collaborative – multi-agent system (MAS). This trend is gaining attention due to its potential to address complex tasks that require both autonomy and teamwork across various agents. 

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Unlike individual AI agents that operate independently, multi-agent systems (MAS) consist of multiple AI agents working together, each with specific roles and capabilities, to achieve a shared goal. These systems are valuable in scenarios where tasks are too complex or expensive to manage for a single AI agent. 

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Some frameworks for multi-agent systems include the following:

• OpenAI Swarm — An experimental framework that supports the development of multi-agent systems. It simplifies the orchestration of multiple agents with a simple and clear interface.
• CrewAI — An open-source multi-agent framework that enables agents to communicate and complete tasks as a team. 
• AutoGen — A framework developed by Microsoft to build conversational agents. It lets agents generate and improve solutions through iterative collaboration.

Security risks associated with MAS include the following:

• Multi-agent systems face security risks like DoS attacks, insecure communication channels, fake agents or services, and manipulation of event logs, which can disrupt operations and compromise data integrity.
• Agents can be vulnerable to unauthorized access, message injection, identity spoofing, and manipulation from other agents or hosts that compromise task accuracy and collaboration.

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3. Proliferation of Small Models & Local Deployment

The rise of small, efficient models is making advanced AI more accessible. Compact models bringpowerful capabilities to a broader audience. Unlike large models that require extensive computational resources, these smaller models can run on basic hardware, such as a single GPU. This enables developers, startups, academics, and small businesses to leverage AI without high costs.

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This shift to small models and local deployment democratizes AI by lowering the resources needed for training and operation. With reduced costs and simpler requirements, a broad range of users now have access to powerful AI tools.

However, this trend brings specific security concerns:

• Local deployment on numerous devices increases potential entry points for attackers.
• The rapid growth and accessibility of these technologies will likely empower less-skilled attackers to execute previously unreachable attacks.
• Physical security risks may increase as generative AI is integrated into more physical systems, including critical infrastructure.

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4. Computer Use by AI Models

A major step forward in AI capabilities is the recent beta release of Claude 3.5 Sonnet’s “computer use” feature, designed to operate a computer much like a human would. Through an API, developers can now instruct Claude to perform computer tasks such as moving a cursor, clicking buttons, typing text, and even analyzing screenshots. Once a command is set up, Claude can complete each action automatically. 

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This advancement is drawing attention across the tech world as it could redefine the scope of AI automation, allowing average users to offload even more digital tasks to AI systems. Claude’s computer-use feature is currently experimental and expected to evolve quickly as developers test and refine it. 

Given this feature’s initial success, other companies are likely to release similar capabilities soon.

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However, a few security concerns also arise with this feature:

• With access to personal data and computer operations, there’s a risk of sensitive information being mishandled.
• AI operating independently may perform unauthorized actions, posing a risk of identity misuse or fraud.
• AI-driven automation could create new channels for distributing spam or spreading false information.

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5. AI Governance Solutions 

With AI being used in highly regulated sectors like healthcare and finance, the need for tools to ensure responsible and ethical AI use is growing. AI governance platforms provide essential safeguards to help organizations maintain transparency, fairness, and accountability in AI operations. 

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By 2028, organizations implementing AI governance solutions are expected to see a 30% increase in customer trust and a 25% boost in regulatory compliance compared to their peers.

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Several standards have been developed to guide organizations in the ethical and safe use of AI, such as:

• ISO/IEC 23894: This international standard provides guidelines for managing AI risks, covering aspects like fairness, accountability, and transparency in AI.

• NIST AI Risk Management Framework: Developed by the U.S. National Institute of Standards and Technology, this framework supports organizations in identifying and managing risks associated with AI systems.

• EU AI Act: Proposed by the European Union, this regulatory framework categorizes AI risks and establishes compliance requirements based on system impact, focusing on ensuring safety and human rights.

AI governance solutions can benefit organizations in the following ways:

• Evaluate AI systems for risks like bias, privacy issues, and unintended impacts, ensuring they align with governance standards.
• Oversee AI model life cycles, ensuring all stages meet compliance standards to minimize risks.
• Track AI usage and audit AI decisions to ensure it operates transparently and ethically.

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6. Multimodal AI

Multimodal AI is transforming how AI systems process and respond to the world. Unlike traditional AI, which handles only one data type, multi-modal AI can combine text, images, and audio inputs. This helps systems understand diverse information types together. This approach brings AI closer to human-like perception and enhances tools like virtual assistants and content creation apps.

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Some leading frameworks in multi-modal AI include: 

• OpenAI’s CLIP – which processes text and images together
• DeepMind’s Perceiver – which can handle various data types in a single model
• Google MUM – a model known for its versatile handling of multi-modal inputs. 

Multimodal frameworks drive new capabilities across industries. For example, multi-modal AI can analyze medical images alongside patient histories to improve diagnostic accuracy in healthcare. Moreover, it allows non-specialists to design or code, expanding roles without requiring specialized skills in the workplace.

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However, multi-modal AI introduces specific security challenges:

• Handling multiple data types introduces multiple data touchpoints and processing pipelines, increasing the risk of unauthorized access to or misuse of sensitive information.
• Threat actors can target vulnerabilities in different input types, like text, images, or audio, making it harder to secure these systems comprehensively.
• Each data type requires unique security protections. For example, introducing anonymity in images requires techniques different than securing text, multiplying the need for specialized security solutions.

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Navigating AI Security in 2025

The AI trends highlight both the exciting potential and risks of new AI developments. For companies, success in 2025 will depend on their ability to implement comprehensive security strategies that align with these evolving technologies. Organizations must act now to build security frameworks that can adapt to the rapidly changing AI landscape while maintaining operational efficiency.

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By partnering with Pillar Security, organizations can ensure their AI implementations are protected by practical, enforceable security measures that go beyond theoretical frameworks. Our comprehensive approach to testing and validation enables leading companies to deploy AI solutions that remain secure and trustworthy in real-world applications, aligning with the evolving security demands of 2025 and beyond.

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