Cybersecurity experts have uncovered a stealthy tactic where attackers bypass Windows defenses by running concealed Linux virtual machines using QEMU. Researchers warn that these hidden environments allow threat actors to maintain persistent access, steal sensitive data, and even deploy ransomware.
Salesforce has introduced what it describes as the most crucial architectural overhaul in its 27-year history, launching a new initiative called “Headless 360.” The update is designed to allow artificial intelligence agents to control and operate the company’s entire platform without requiring a traditional graphical interface such as a dashboard or browser.
The announcement was made during the company’s annual TDX developer conference in San Francisco, where Salesforce revealed that it is releasing more than 100 new developer tools and capabilities. These tools immediately enable AI systems to interact directly with Salesforce environments. The move reflects a deeper shift in enterprise software, where the rise of intelligent agents capable of reasoning and executing tasks is forcing companies to rethink whether conventional user interfaces are still necessary.
Salesforce’s answer to that question is direct: instead of designing software primarily for human interaction, the platform is now being rebuilt so that machines can access and operate it programmatically. According to the company, this transformation began over two years ago with a strategic decision to expose all internal capabilities rather than keeping them hidden behind user interfaces.
This shift is taking place during a period of uncertainty in the broader software industry. Concerns that advanced AI models developed by companies like OpenAI and Anthropic could disrupt traditional software business models have already impacted market performance. Industry indicators, including software-focused exchange-traded funds, have declined substantially, reflecting investor anxiety about the long-term relevance of existing SaaS platforms.
Senior leadership at Salesforce has indicated that the new architecture is based on practical challenges observed while deploying AI systems across enterprise clients. According to internal insights, building an AI agent is only the initial step. Organizations also face ongoing challenges related to development workflows, system reliability, updates, and long-term maintenance.
To address these challenges, Headless 360 is structured around three foundational pillars.
The first pillar focuses on development flexibility. Salesforce has introduced more than 60 tools based on Model Context Protocol, along with over 30 pre-configured coding capabilities. These allow external AI coding agents, including systems such as Claude Code, Cursor, Codex, and Windsurf, to gain direct, real-time access to a company’s Salesforce environment. This includes data, workflows, and underlying business logic. Developers are no longer required to use Salesforce’s own integrated development environment and can instead operate from any terminal or external setup.
In addition, Salesforce has upgraded its native development environment, Agentforce Vibes 2.0, by introducing an “open agent harness.” This system supports multiple agent frameworks, including those from OpenAI and Anthropic, and dynamically adjusts capabilities depending on which AI model is being used. The platform also supports multiple models simultaneously, including advanced systems like Claude Sonnet and GPT-5, while maintaining full awareness of the organization’s data from the start.
A notable technical enhancement is the introduction of native React support. During demonstrations, developers created a fully functional application using React instead of Salesforce’s traditional Lightning framework. The application connected to Salesforce data through GraphQL while still inheriting built-in security controls. This significantly expands front-end flexibility for developers.
The second pillar focuses on deployment. Salesforce has introduced an “experience layer” that separates how an AI agent functions from how it is presented to users. This allows developers to design an experience once and deploy it across multiple platforms, including Slack, mobile applications, Microsoft Teams, ChatGPT, Claude, Gemini, and other compatible environments. Importantly, this can be done without rewriting code for each platform. The approach represents a change from requiring users to enter Salesforce interfaces to delivering Salesforce-powered experiences directly within existing workflows.
The third pillar addresses trust, control, and scalability. Salesforce has introduced a comprehensive set of tools that manage the entire lifecycle of AI agents. These include systems for testing, evaluation, monitoring, and experimentation. A central component is “Agent Script,” a new programming language designed to combine structured, rule-based logic with the flexible reasoning capabilities of AI models. It allows organizations to define which parts of a process must follow strict rules and which parts can rely on AI-driven decision-making.
Additional tools include a Testing Center that identifies logical errors and policy violations before deployment, custom evaluation systems that define performance standards, and an A/B testing interface that allows multiple agent versions to run simultaneously under real-world conditions.
One of the key technical challenges addressed by Salesforce is the difference between probabilistic and deterministic systems. AI agents do not always produce identical results, which can create instability in enterprise environments where consistency is critical. Early adopters reported that once agents were deployed, even small modifications could lead to unpredictable outcomes, forcing teams to repeat extensive testing processes.
Agent Script was developed to solve this problem by introducing a structured framework. It defines agent behavior as a state machine, where certain steps are fixed and controlled while others allow flexible reasoning. This approach ensures both reliability and adaptability.
Salesforce also distinguishes between two types of AI system architectures. Customer-facing agents, such as those used in sales or support, require strict control to ensure they follow predefined rules and maintain brand consistency. These operate within structured workflows. In contrast, employee-facing agents are designed to operate more freely, exploring multiple paths and refining their outputs dynamically before presenting results. Both systems operate on a unified underlying architecture, allowing organizations to manage them without maintaining separate platforms.
The company is also expanding its ecosystem. It now supports integration with a wide range of AI models, including those from Google and other providers. A new marketplace brings together thousands of applications and tools, supported by a $50 million initiative aimed at encouraging further development.
At the same time, Salesforce is taking a flexible approach to emerging technical standards such as Model Context Protocol. Rather than relying on a single method, the company is offering APIs, command-line interfaces, and protocol-based integrations simultaneously to remain adaptable as the industry evolves.
A real-world example surfaced during the announcement demonstrated how one company built an AI-powered customer service agent in just 12 days. The system now handles approximately half of customer interactions, improving efficiency while reducing operational costs.
Finally, Salesforce is also changing its business model. The company is shifting away from traditional per-user pricing toward a consumption-based approach, reflecting a future where AI agents, rather than human users, perform the majority of work within enterprise systems.
This transformation suggests a new layer in strategic operations. Instead of resisting the rise of AI, Salesforce is restructuring its platform to align with it, betting that its existing data infrastructure, enterprise integrations, and accumulated operational logic will continue to provide value even as software becomes increasingly autonomous.
A single photograph captured in a remote forest over a decade ago has become central to one of the most complex legal questions of the digital age: what happens when creative work is produced without direct human authorship? The answer now carries long-term consequences for artificial intelligence, creative industries, and ownership rights in the modern world.
The image in question originated in 2011, when wildlife photographer David Slater was documenting crested black macaques in Indonesia. These monkeys are not only endangered but also known for their highly expressive faces, making them attractive subjects for photography. However, Slater faced difficulty capturing close-up shots because the animals were wary of human presence.
To work around this, he positioned his camera on a tripod, enabled automatic focus, and used a flash, allowing the monkeys to approach and interact with the equipment without feeling threatened. His approach relied on curiosity rather than control. Eventually, one macaque handled the camera and pressed the shutter button while looking directly into the lens. The resulting image, widely known as the “monkey selfie,” appeared almost intentional, with the animal’s expression resembling a posed portrait.
While the photograph initially brought attention and recognition, it soon triggered an unexpected legal dispute. The core issue was deceptively simple: if a photograph is not taken by a human, can anyone claim ownership over it?
The situation escalated when the image was uploaded to Wikipedia, making it freely accessible worldwide. Slater objected to this distribution, arguing that he had lost approximately £10,000 in potential earnings because the image could now be used without payment. However, the Wikimedia Foundation refused to remove the photograph. Its reasoning was based on copyright law, which generally requires a human creator. Since the image was captured by an animal, the organisation classified it as public domain material.
This interpretation was later reinforced by the U.S. Copyright Office, which formally clarified that works produced without human authorship cannot be registered. In its guidance, the office explicitly listed a photograph taken by a monkey as an example of ineligible material, establishing a clear precedent.
The dispute took another unusual turn when People for the Ethical Treatment of Animals filed a lawsuit attempting to assign copyright ownership to the macaque itself. Although framed as a legal claim over the photograph, the case was widely interpreted as an effort to establish broader legal rights for animals. After several years of legal proceedings, a court dismissed the case, concluding that animals do not have the legal capacity to initiate lawsuits.
Legal experts later observed that, although the case focused on animal authorship, it introduced a broader conceptual challenge that would become more relevant with the rise of artificial intelligence. According to intellectual property lawyer Ryan Abbott, the debate could easily extend beyond animals to machines capable of producing creative outputs.
This possibility became reality when computer scientist Stephen Thaler attempted to secure copyright protection for an image generated by his AI system, DABUS. Thaler described the system as capable of independently producing ideas, arguing that it should be recognised as the sole creator of its output. He characterised the system as exhibiting a form of machine-based cognition, though this view is strongly disputed within the scientific community.
Despite these claims, the Copyright Office rejected the application, applying the same reasoning used in the monkey selfie case. Because the work was not created by a human, it could not qualify for copyright protection. This rejection led to a legal challenge that progressed through multiple levels of the U.S. judicial system.
When the case reached the Supreme Court of the United States, the court declined to hear it, leaving lower court rulings intact. The outcome effectively confirmed that, under current U.S. law, works generated entirely by artificial intelligence cannot be owned by anyone, including the developer of the system or the individual who prompted it.
This position has reverberating implications for the creative economy. Copyright law exists to allow creators and organisations to control and monetise their work. Without ownership rights, it becomes difficult to build sustainable business models around fully AI-generated content. Legal scholar Stacey Dogan noted that this limitation reduces the likelihood of a future where machine-generated content completely replaces human-created media.
At the same time, the rapid expansion of generative AI tools continues to complicate the landscape. These systems function by analysing large datasets and producing outputs based on user instructions, often referred to as prompts. While they can generate text, images, and video at scale, their outputs raise questions about originality and authorship, particularly when human involvement is minimal.
Recent industry developments illustrate this uncertainty. Experimental AI-generated content has attracted large audiences online, suggesting a level of public interest, even if motivations such as novelty or criticism play a role. However, some technology companies have begun reassessing their AI content strategies, particularly where ownership and profitability remain unclear.
Expert opinion on the value of fully AI-generated content remains divided. Some specialists argue that such content lacks depth or authenticity, while others view AI as a useful tool for supporting human creativity rather than replacing it. This perspective positions AI as a collaborator rather than an independent creator.
Legal approaches also vary internationally. In the United Kingdom, copyright law allows ownership of computer-generated works by assigning authorship to the individual responsible for arranging their creation. However, this framework is currently being reconsidered as policymakers evaluate whether it remains appropriate in the context of modern AI systems.
One of the most complex unresolved issues involves hybrid creation. When humans actively guide, refine, and edit AI-generated outputs, determining ownership becomes less straightforward. A notable example involves an AI-assisted artwork that won a competition after extensive prompting and editing, raising questions about how much human contribution is required for copyright protection.
This debate is not entirely new. When photography first emerged, similar concerns were raised about whether cameras, rather than humans, were responsible for creative output. Over time, legal systems adapted by recognising the role of human intention and decision-making. Artificial intelligence now presents a more advanced version of that same challenge.
For now, the legal position in the United States remains clear: without meaningful human involvement, creative works cannot be protected by copyright. However, as AI becomes increasingly integrated into creative processes, the distinction between human and machine contribution is becoming more difficult to define.
What began as an unexpected interaction between a monkey and a camera has therefore evolved into a defining case in the global conversation about creativity, ownership, and technology. The decisions made in courts today will shape how creative work is produced, distributed, and valued in the future.