Salesforce Agentforce Update: How It Impacts US Sales Teams

Agent care is not replacing salespeople; it’s making them better. Sales force agent player: Agent Force helps sales teams close deals faster by reducing busywork with autonomous digital support. For example, it has saved sales forces over fifty-five million hours through automated call and conversation summaries.  

Why it matters: As the first users, sales forces, meaning sales teams, are not just benefiting from the technology; they are also testing it to make sure it works well for real-world selling and is ready to deliver value for customers.  

Driving the news: sales forces and the rollout of AgentForce, led by the sales force.  

  • 800,000+ leads and contacts added to sales calendars through AI.  
  • 440,000+ sales activities logged monthly without human intervention.  
  • 75,000+ AI-generated summaries to accelerate sales planning and prospecting.  
  • 43,000+ personalized emails generated through their targeted outreach.  

Go deeper: Today, sales reps spend only 30% of their time actually selling. The rest goes to tasks like research, data entry, cold calling, and scheduling. Agent Force changes this by using autonomous AI agents to free up reps so they can focus on selling and building relationships. Here’s how Agent Force helps:  

  • Sales department: Agentforce fits into a rep’s daily routine by drafting personalized emails, summarizing calls, and automatically logging activities. Unlike standard tools, it uses reasoning to fill in new information, providing sales teams with insights that help them achieve better results.  
  • Sales coaching column Agent Force provides sellers with personalized, on-demand coaching. It guides them through practice scenarios and provides real-time feedback by using CRM data. The coaching is tailored to each rep’s pipeline, products, and customers. This means the advice is based on real sales situations, not just generic templates. Whether reps are refining their pitch, handling objections, or negotiating deals  

What they are saying.  

  • “At Salesforce, we power our business on our own technology. Our trinity of Agentforce, Data Cloud, and Customer 360 provides sellers with real-time AI-driven insights that create a competitive edge for our teams and our customers. Our sellers are automating routine work, receiving live coaching, and deepening customer relationships with greater precision with Agentforce.” Conor Marsden, President of Sales at Salesforce.  
  • “AgentForce has truly become my partner in sales. From prepping talk notes to sending personalized follow-ups, it helps me stay focused and organized. What really sets it apart is the real-time coaching, giving me context-driven feedback to improve in the moment. AgentForce is like having an assistant and a coach wrapped into one, enabling me to focus on what matters most and make a real impact for my customers.” Haley Gault, Account Executive at Salesforce.  

The Agent Force, Data Cloud, and Customer 360 apps work together to give sellers real-time, AI-driven insights. This creates a competitive edge for both our teams and our customers. Conner Marsden, President of Sales at Salesforce.  

What’s new? 

Making it faster to get AI into production: 

  • Expanding the agent scanners column. Automotive discovery now includes NCP servers and new platforms like Amazon Bedrock, Microsoft Foundry, and Vultr. This speeds up visibility and registration for AI assets using secure OAuth authentication.  
  • Visual authoring panels, a new drag-and-drop interface, and new soft guides to help map workflows and human checkpoints. This makes it easier for developers to find the right agents and set up their products.  
  • MCP bridge: make existing APIs ready for agents by enabling an MCP at scale. You can also add enterprise-level security and rate limiting without changing any code.  
  • Informatica-Hosted MCPs: bring Informatica’s data quality and governance MCP servers directly into your workflows. These are automatically available in the agent registry, so every agent interaction starts with trusted, well-governed data.  

Bringing Rigor And Oversight To Agent Interactions And Multi-Agent Orchestration 

Agent script for agent broker: Apply the same guide that covers the course for the agent first to the agent broker. You can set fixed ends of rules while elements manage the reason in between. This combination of role-based and trusted workflows yields more consistent, reliable results.

Source: Salesforce Advances Agent Fabric: New Guided Determinism and Governance Controls to Scale Multi-Vendor AI Faster  

AWS vs Azure vs GCP: Which Saves more for US SMBs (2026)?

In 2026, US businesses have moved past experimenting with AI and are now focused on getting real financial results. Companies want more than just test projects. They need platforms that fit closely with their main business goals and help them grow and save money. For US small and medium-sized businesses, the main question is which cloud provider makes it easiest to get started without creating extra complexity. Right now, the top position is AWS versus Azure versus GCP, Coron, which saves US SMBs more. (2026) Choosing the right platform is a key step that determines a company’s future success and how quickly it can move from collecting data to taking action automatically.  

Tactical Cost Efficiency For Small Businesses 

Controlling costs is now about showing real results by using resources wisely, not just cutting budgets. Many US assemblies pick a provider based on the software they already use and the specific needs of their business. AWS is still the top choice for small businesses, with about 45% spending less than $60,000 annually. This is mostly because AWS has flexible pay-as-you-go pricing and a wide range of unique services, making it easier for small teams to grow without high upfront costs. When comparing AWS, Azure, and GCP, the best option often comes down to how many AI tools a team can use to avoid expensive custom work.  

AWS is still the main choice for most cloud-first startups, but Azure is catching up fast with its added benefit programs. Small businesses using Microsoft 365 can save money by leveraging their existing licenses. Even though it has a lower market share, it often offers a five to ten percent discount on computing through automatic sustained use discounts, which makes GCP a good fit for data-focused SMBs that need steady power for non-unloaded loads. To keep cloud costs predictable, businesses need to understand how much they use AI for training versus for running models.  

AWS Versus Azure Versus GCP: Which Suits More for US SMBs (2026)? 

To figure out which provider saves you more on your assemblies in 2026, look at the total cost of ownership, not just the hourly price. A virtual machine, AWS gives the most detailed control with its custom chips like Trainium3 and Inferentia2, which can cut AI inference costs by up to 40% compared to regular GPUs. For a small business using AI for customer service, these savings can make a project profitable before a cost burden exceeds 340. AWS services can be complex, requiring the hiring of a DevOps specialist, which could reduce savings for smaller teams.  

Microsoft Azure takes a different approach by offering a more managed service path, thanks to its partnership with OpenAI and strong integration with the Power Platform. Small firms can use Copilot Studio to build autonomous agents with minimal coding requirements, reducing development time and costs. For companies focused on automating internal tasks, Azure’s bundled pricing often leads to a better return on investment. The main savings come from indeed fewer developer hours, not the cheaper hardware. This makes Azure a top choice for businesses that want quick setup and easy integration rather than deep technical customization.  

Google Cloud Platform is good for data-intensive small businesses, offering some of the best big data and container tools available. Its Vertex AI platform makes managing machine learning easier, so one data scientist can do the work of a whole team. With BigQuery ML, small firms can run machine learning directly on their data, avoiding costly data transfer fees between clouds. This in-place analytics approach is how GCP helps US SMBs save money, especially those that need instant insights. The main savings come from eliminating unnecessary data processing steps and reducing delays.  

Maximizing ROI via Specialized AI Infrastructure 

In 2026, AI platforms are shifting from just storing models to actually taking action. Top platforms now include features like a memory layer policy controller to keep automated agents within company rules. This is especially important for US SMBs that don’t have big legal teams to watch every automated process. Choosing a platform with built-in compliance and security helps small businesses avoid costly information breaches or fines. Now, the return on investment from safety and value is just as important as the return from speed and productivity.  

The platform you choose also affects how quickly your company can innovate. Since each cloud supports different skill sets, AWS is popular with web developers and system admins, while GCP is favored by data engineers and AI experts. Azure makes sense as the top choice for businesses migrating from legacy on-premises systems to the cloud. US SMEs need to align their cloud choice with their team’s skills to ensure they can use what they are paying for. For example, a partial GCP setup won’t help much if your IT staff only knows Windows Server.  

The Future of the Sovereign SMB Cloud 

By the end of the decade, the line between software and businesses will blur as they become more connected. Soon, the main question won’t be which single cloud serves best, but how to manage multiple clouds together. Some assemblies already use the best parts of each cloud provider, such as Azure for identity and communication, and GCP for analytics. This approach stops businesses from being locked into a single vendor and lets them leverage each provider’s strengths. It helps companies stay efficient no matter how the market changes.  

In the future, much of our business work will be handled automatically and reliably by AI. The goal of the sovereign AI movement is to create organizations that are always learning and ready to help people. By choosing the right platform now, US small businesses are preparing for a future in which their logical thinking is their greatest asset. The road towards a strong partnership between people and machines has already begun, powered by the technology we’ve created. 

Source: AWS News Blog 

Best AI GPU Cloud Providers in the US: Cost Comparison (2026)

In the past, data centers mainly stored, retrieved, and processed information. Now, with generative and agentic AI, they have become AI token factories. Their main job is running AI inference, using intelligence as tokens.  

This change means we need to rethink how we measure the economics of AI infrastructure, including the total cost of ownership (TCO) and open market. Many companies still focus too much on chip specs, compute costs, and FLOPs per dollar.  

The key difference to focus on is:  

  • Compute cost is the amount companies pay for AI infrastructure, whether they rent it from the cloud or own it themselves.  
  • FLOPS per dollar measures how much raw computing power a company gets for each dollar. But raw compute is not the same as actual token output.  
  • Cost per token is the total amount a company spends to produce each token, usually shown as cost per million tokens.  

The first two are just input metrics. Focusing on inputs when your business depends on outputs is a basic mismatch.  

The cost per token shows whether a company can scale AI profitably. It’s the only TCO metric that directly reflects hardware, software, ecosystem support, and real-world use. NVIDIA offers the lowest cost per token in the industry.  

What Factors Help Lower Token Cost? 

To optimize token costs, we need to examine how the cost per million tokens is calculated.  

When looking at this equation, many companies focus on the numerator column for cost per GPU per hour in the cloud, which is the provider’s on-premises hourly rate. It’s the only cost of spreading out the infrastructure expense. But the real way to lower token cost is to maximize the number of tokens produced.  

That denominator carries huge business implications.  

  • Minimizing token cost: as you increase token output, the cost per token drops, boosting profit margins for every interaction.  
  • Maximizing revenue: delivering more tokens per second also means more tokens per network. This lets you get more intelligence from your AI products and services, increasing revenue from the same infrastructure.  

If you only focus on the numerator, you miss what really drives results. It’s like an iceberg. The numerator is visible above the surface, but the denominator is hidden below and holds the key shackles to well-coordination. To evaluate your infrastructure well, you need to look deeper.  

Surface-Level Inquiry 

  • What is the cost per GPU hour?  
  • What are the peak petaflops and high bandwidth memory capacity?  
  • What are the HLOPS per dollar?  

In-Depth Cost Analysis 

  • What is the cost per million tokens? Specifically, what is the cost per million tokens for large-scale mixture-of-experts (MOE) reasoning models, which are the most widely deployed type of AI models?  
  • What is the delivered token output turnover for enterprises deploying this architecture, where capital commitment to land power and infrastructure is substantial? Maximizing intelligence produced turnover is critical.  
  • Can the scale interconnect handle the all-to-all traffic of MOE models?  
  • Is FP4 precision supported? Can the inference stack make use of FP4 while maintaining high accuracy?  
  • Does the inference runtime support speculative decoding to improve multi-token prediction and increase user interactivity?  
  • Does the serving layer support disaggregated serving, KB-aware routing, KB cache offloading, and other optimizations?  
  • Does the platform support the unique workload requirements of AI, including ultra-low latency, high throughput, and long input sequences? Does the platform support the full lifecycle from training and post-training to high-scale inference across all model architectures to ensure infrastructure flexibility and high utilization?  

All these algorithms, hardware, and software optimizations need to work together. If they don’t, the denominator drops. A cheaper GPU that produces fewer tokens per second actually raises your cost per token. The best AI infrastructure gets every part right, so each optimization supports the others.  

Why Is Cost Per Token Much More Important Than FLOPS Per Dollar? 

Data from the DeepSeek R1 AI model shows the gap between theory and real business results.  

If you only look at compute cost, the NVIDIA Blackwell platform seems about twice as expensive as the NVIDIA Hopper platform, but compute cost doesn’t reflect what you get for your money. FLOPs per dollar suggests Blackwell is twice as good as Hopper. In reality, Blackwell delivers over fifty times more token output per watt and nearly thirty-five times lower cost per million tokens.  

Metric  NVIDIA Hopper (HGHH200)  NVIDIA Blackwell (GB300 NVL72)  NVIDIA Blackwell relative to Hopper  
Cost per GPU per Hour ($).  $1.41  $2.65  2x  
FLOP, per Dollar (PFLOPS)  2.8  
 		5.6  
 		2x  
Tokens per second per GPU  90  6,000  65X  
Tokens per second per MW  54K  2.8M  
 		50 X  
Cost: twelve million tokens ($)  $4.20  $0.12  35 X Lower  
    

Note: Data is sourced from NVIDIA analysis, as in the Insurance X V2 benchmark.  

This significant difference shows that NVIDIA Blackwell offers much greater business value than the older Hopper generation, despite any increase in system costs.  

How to Choose the Right AI Infrastructure 

Looking at AI infrastructure only in terms of compute cost or theoretical FLOPS per dollar does not give a true picture of inference economics. To really understand the revenue potential and profitability, it is better to focus on cost per token and the number of tokens delivered.  

NVIDIA offers the lowest token cost and the highest token throughput in the industry by carefully designing its compute, networking, memory, storage, software, and partner technologies to work together on the inference to open source inference software like vLLM, SGLang, NVIDIA TensorRT-LLM, and NVIDIA Dynamo on the NVIDIA platform help increase token output and lower the cost per token over time, even after the interception is in place.  

Top cloud providers and NVIDIA partners are already offering these benefits at scale. Companies like CoreWeave, Nebius, Nscale, and Together AI use NVIDIA Blackwell infrastructure and have optimized their systems to give businesses the lowest token cost available today, backed by NVIDIA hardware, software, and ecosystem working together.

Source: Rethinking AI TCO: Why Cost per Token Is the Only Metric That Matters 

Salesforce vs Copilot: Which AI Platform Delivers ROI?

The United States business sector examines which artificial intelligence platforms deliver tangible financial benefits during implementation across various company functions. The two main enterprise AI solutions, Salesforce Einstein AI and Microsoft Copilot, demonstrate different approaches to artificial intelligence within their respective systems, which combine customer relationship management and productivity software.  

The two platforms provide benefits by improving work processes, while their automated systems enable better decision-making. The two systems demonstrate their differences through their ability to deliver tangible business results, including financial savings, increased sales, and improved productivity.  

The Shift Toward ROI-Driven AI Adoption  

Companies began using artificial intelligence to test new technologies through their research. The present day requires businesses to measure their actual results. Businesses assess artificial intelligence solutions based on their ability to boost efficiency, reduce costs, and improve decision-making processes.  

The new requirements require organizations to examine their deployment methods, their ability to connect different systems, and their capacity to deliver benefits over time. Assessors of Salesforce Einstein AI and Microsoft Copilot must evaluate both the product features and the system’s capacity to deliver stable, scalable performance.  

Organizations are increasingly prioritizing solutions that align with their existing workflows and provide clear performance metrics.  

Salesforce Einstein: AI for Customer-Centric Operations  

Salesforce Einstein AI exists to improve customer relationship management by integrating artificial intelligence into Salesforce platforms. The system offers three main functions: predictive analytics, automated insights, and customer-specific interactions.  

Sales teams can use Einstein to predict sales possibilities while it provides guidance on subsequent actions and handles their standard operational duties. The system enables marketing teams to improve campaign performance by enabling more efficient audience targeting.  

The platform enables organizations to leverage their existing customer data through tightly integrated CRM connections, improving decision-making. This can lead to improved conversion rates and stronger customer relationships.  

Microsoft Copilot: AI Across Productivity Workflows  

Microsoft Copilot takes a broader approach by integrating AI into widely used productivity tools such as Word, Excel, Outlook, and Teams. This allows users to automate tasks, create content, and perform data analysis within their known work environments.  

Copilot enables users to perform various tasks, including administrative work, data analysis, and content creation, by supporting multiple applications.  

Microsoft Copilot embeds artificial intelligence into everyday workflows to improve productivity, helping workers spend less time on repetitive tasks and more time on important work activities.  

Comparing ROI: Targeted vs Broad Impact  

The ROI of each platform depends primarily on its operational use across the organization. Salesforce Einstein AI provides specific advantages to customer-facing operations, which sales and marketing teams find most beneficial.  

Organizations measure their impact through three main indicators: increased revenue, better customer retention, and improved sales processes. The benefits from these systems deliver substantial advantages to companies that depend on CRM systems.  

Microsoft Copilot provides organizations with broader productivity benefits that extend across all their departments. The return on investment from this system is realized through three benefits: time savings, reduced manual work, and improved teamwork.  

Organizations must evaluate their requirements for specialized AI systems or general productivity enhancement tools.  

Implementation and Integration  

The implementation process needs to be simple because it is the main factor that determines return on investment. The Salesforce platform needs Salesforce Einstein AI to function as a unified system, enabling current users to use it more efficiently.  

Organizations that lack a strong Salesforce foundation will struggle because they need to spend more money and face more complex implementation processes.  

Microsoft Copilot receives advantages from its integration with Microsoft 365, which most enterprises use as their standard software. The learning process becomes easier, helping people adopt new skills faster. The degree of system integration determines how quickly organizations can realize value from their existing platforms.  

Measuring Productivity Gains  

The assessment of the effects of artificial intelligence tools needs to be quantifiable because it is a critical factor in return-on-investment analysis. The Salesforce Einstein AI system delivers sales performance metrics, customer engagement data, and tools for measuring campaign success. The insights enable organizations to monitor their progress while using data analysis to inform their decision-making.  

Microsoft’s Copilot tool tracks productivity through three key metrics: time saved, number of tasks completed, and workflow efficiency. While most of these metrics do not have a direct revenue impact, they are linked to overall operational performance and reduced costs. 

Cost Considerations  

The cost plays a vital role in calculating return on investment. The two platforms use subscription-based pricing, charging different rates based on the user’s selected features and usage level. Implementing Salesforce Einstein AI requires organizations to spend more on their customer relationship management system and customization, resulting in higher initial expenses.  

Many organizations prefer Microsoft Copilot because it is an extra feature that enhances their current Microsoft 365 subscriptions. Organizations need to assess both the upfront expenses and the future benefits that each system will deliver to their operations.  

Challenges in AI Adoption  

The two platforms have the potential for success but face challenges that prevent their users from adopting them. The three factors that hinder their adoption, together with user resistance, training requirements, and change management needs.  

The effective use of AI tools demands that organizations develop a specific implementation plan that requires continuous support.  

The two systems, Salesforce Einstein AI and Microsoft Copilot, operate by leveraging user interaction with high-quality data.  

Conclusion: Choosing the Right AI Investment  

The choice of Salesforce Einstein AI or Microsoft Copilot will depend on what an organization cares about most. Salesforce Einstein provides excellent solutions for customer service, and Microsoft Copilot enhances productivity through the power of Microsoft Office products. 

Sales and customer engagement companies should use Salesforce because it provides better return on investment for their business needs. Companies that require efficient operations across different areas should choose Copilot as their solution.  

The capacity to produce tangible outcomes will determine which AI investments succeed as enterprises assess their AI funding decisions.

Source: Introducing Salesforce Headless 360. No Browser Required.

Post-Quantum Cryptography Guide for US Businesses (2026)

Most companies today use encryption to protect sensitive data, such as their clients’ data, financial records, and internal communications. But soon, there will be a big change. 

Quantum computers will be able to break common types of encryption and render current security systems useless. Current security systems would be rendered useless by quantum computers capable of breaking encryption. Even though quantum computing is still under development at scale, we are already facing that threat. While companies can store encrypted data using digital encryption, in a few years’ time, they could decrypt it using quantum computing. 

That is why the National Institute of Standards and Technology is working to establish post-quantum cryptography (PQC) standards so businesses can be ready to adopt this new form of encryption. 

What Is Post-Quantum Cryptography? 

Post-quantum cryptography (PQC) is a new form of encryption developed to withstand attacks by quantum computers. Traditional cryptography relies on complex mathematics that can be quickly solved by quantum computers, whereas PQC relies on mathematical algorithms that would remain secure in a quantum computer’s environment. 

In simple terms, post-quantum cryptography is a method to ensure the long-term protection of encrypted data. 

Types of traditional encryption include: 

  • RSA (based on simplifying prime numbers into their integers) 
  • ECC (based on problems related to the elliptic curve) 
  • Examples of post-quantum cryptography include: 
  • Lattice-based encryption 
  • Hash-based signatures/verify 
  • Code-based encryption 

The algorithms and methods for PQC require a quantum computer to spend more time finding ways to decrypt or establish mathematically valid relationships than traditional forms of encryption. 

Why Businesses Should Care Now 

It’s easy to assume quantum threats are far off, but the risk timeline doesn’t work that way. Data encrypted today could be vulnerable tomorrow. 

This is known as the “harvest now, decrypt later” problem. 

Key concerns include: 

  • Long-term sensitive data exposure 
  • Regulatory risks as standards evolve 
  • Loss of customer trust in case of future breaches 

In industries such as finance, healthcare, and defense, delayed adoption can create serious long-term vulnerabilities. 

Why Quantum Computers can violate present encryption 

Understanding urgency with quantum computers, modification of the rules. 

Every aspect of traditional Encryption relies on problems considered hard for classical computers; however, quantum computers are able to resolve those same issues exponentially quicker due to their algorithmic methodology (i.e., Shor’s Algorithm) 

Impact of current systems 

  • RSA-encrypted systems become breakable. 
  • ECC-based systems become prone. 
  • The ability to exchange secure keys becomes reduced. 

Therefore, encryption protocols such as HTTPS and VPNs are highly susceptible to compromise. 

NIST standardization of post-quantum cryptography 

The National Institute of Standards and Technology has assumed global leadership in developing standards for post-quantum Cryptographic Algorithms. 

Milestones achieved: 

  • Selected candidate Algorithms for posting 
  • Released draft guidelines for use 
  • Inspiring companies to begin planning on transitioning. 

The creation of these standards will provide the basis for all future encryption systems. 

Post-Quantum Cryptography Transition Framework 

Assessment Identify vulnerable systems Risk visibility 
Planning Choose PQC-ready solutions Strategic alignment 
Implementation Upgrade encryption systems Future-proof security 
Monitoring Continuous updates Long-term resilience 

Key Challenges in Adoption 

While the need for PQC is clear, transitioning isn’t simple. Businesses face several technical and operational challenges. 

Major barriers include: 

  • Compatibility issues with existing systems 
  • Performance impact of new algorithms 
  • Lack of skilled expertise 
  • Uncertainty around evolving standards 

These challenges make it important to adopt a phased and well-planned approach. 

Conclusion 

Post-quantum cryptography is not just a technical upgrade—it’s a strategic necessity. Businesses that delay adoption risk exposing sensitive data to future threats, even if their systems appear secure today. 

The transition may take years, but the time to start is now. Organizations that prepare early will be better positioned to protect their data, maintain trust, and stay ahead in an evolving threat landscape.

Source: What Is Post-Quantum Cryptography?  

SEC Cyber Disclosure Rules Guide for US Companies (2026)

Cybersecurity incidents will no longer be managed confidentially behind closed doors. Starting in 2026, all U.S. Public Companies will be required to disclose information about material cyber incidents in a timely, structured manner. 

The U.S. Securities and Exchange Commission has implemented new formal rules on cybersecurity disclosures that have changed how public companies report material cyber incidents, shifting them from a strategic decision to a required legal disclosure. The objective of the new rules is to provide investors with timely and accurate disclosures of potential risks that may adversely affect a company’s financial performance. 

The implementation of these rules represents a seismic change for companies, linking cybersecurity directly to their financial reporting and governance. 

Importance of SEC Cybersecurity Disclosure Rules 

Before the rules were implemented, public companies had significant latitude in when and how they disclosed material cyber incidents. This generally resulted in public companies disclosing material cyber incidents on a delayed basis or having inconsistent reporting practices. 

The intent of the new rule is to standardize public companies’ cybersecurity reporting process and thus require: 

  • Strict timelines for reporting material incidents. 
  • Increase the public company’s transparency to its shareholders. 
  • Hold the public company accountable at the executive level. 
  • Failure to comply with these rules can result in regulatory fines, legal exposure, and loss of confidence from their shareholders. 

Key Requirements Under SEC Cyber Disclosure Rules 

Mandatory Disclosure of Cybersecurity Incidents through Form 8-K Filing: 

  •  Companies must disclose important cybersecurity incidents on Form 8-K filing. 
  •  Companies are required to report any material cybersecurity incident within four (4) business days of the date the company determines the incident meets its materiality threshold. 
  •  The report must include the nature and scope of the cybersecurity incident and the company’s assessment of its impact on the company. 
  •  Companies should report the incident without undue delay, except if a delay is necessary for national security purposes. 

This requirement allows general investors to have timely access to information about events that may affect a company’s performance. 

Cybersecurity Risk Reporting on an Annual Basis: 

Companies are also required to report additional information on their cybersecurity practices in their annual filings. Report on risk management strategies, procedures, and measures used to identify and reduce exposure to cybersecurity incidents; and historical loss information for events that have occurred from cybersecurity incidents. 

The creation of this type of disclosure creates a continuous disclosure model rather than a reactive model. 

Governance and Board Oversight 

Cybersecurity is now a corporate board responsibility; therefore, the SEC has specified how a company’s leadership should be involved in overseeing cybersecurity risk. 

  • The Company Board is expected to be directly involved in cybersecurity strategy. 
  • The Company Board is expected to identify the executive(s) responsible for executing the company’s cyber risk strategy. 
  • The Company Board is expected to establish a formal reporting structure for cybersecurity incidents. 

This process enables companies to incorporate cybersecurity into their overall corporate governance. 

Cyber Incident Materiality 

As companies grapple with what constitutes a “material” incident, one primary challenge is assessing how much loss the event would create in terms of finances, operations, reputation, laws and regulations, etc. 

To determine materiality, businesses must establish an assessment process to evaluate these four factors as quickly as possible, so they can provide an accurate report in a timely manner. 

Cyber Security Versus Transparency 

There is a difference between providing total transparency as an SEC requirement versus being required to provide information that could be used to compromise your organization’s cybersecurity. It is up to the organization how they will communicate with their shareholders; however, organizations will use this balance of transparency vs security to: 

  •  Provide communication that is sufficient to inform investors of the risks for the organization, and 
  •  To provide detailed security risk mitigations to ensure their critical systems do not carry an additional level of risk. 

In this manner, organizations are taking a “balanced” approach to their public disclosures in the current high-risk digital age. 

SEC Cyber Disclosure Framework Overview 

Requirement Timeline Purpose Impact 
Incident Disclosure (Form 8-K) 4 days Investor awareness High urgency 
Annual Reporting Yearly Risk transparency Long-term trust 
Governance Disclosure Ongoing Accountability Strategic alignment 
Materiality Assessment Immediate Decision-making Compliance accuracy 

Challenges Companies Are Facing 

Despite straightforward suggestions, several companies fail to act on them. The primary concern is implementing internal procedures within the hard deadlines defined by the SEC. 

Common sticking points include: 

  • Delays in identifying when there has been a cybersecurity event; 
  • Not having a coordinated response between IT, Legal, and Leadership teams; 
  • Inability to evaluate materiality quickly; 
  • Second-guessing because of reputational damage. 

These points help highlight the need for a structured, well-practiced response strategy. 

Conclusion 

The SEC’s rules regarding cybersecurity disclosures are an example of how things are changing due to today’s digital economy, with companies being held accountable for managing cyber risks; investors want to know how companies manage their cyber exposure by illustrating cyber-risk controls in the normal course of doing business, continually, not just when an incident happens. 

By embracing the SEC‘s rules early, organizations will establish compliance and build greater trust with stakeholders in a marketplace that continues to drive toward transparency, providing them with a competitive advantage.

Source: SEC Adopts Rules on Cybersecurity Risk Management, Strategy, Governance, and Incident Disclosure by Public Companies 

Run Llama Models Locally on Mac (US Setup Guide)

Hardware advancements and an increase in available tools have made it much easier for developers to execute large language models locally. Users can run Llama Models on their Macs with Ollama, creating leading-edge AI workflows without relying heavily on cloud-based compute resources. 

On-device AI technology provides multiple benefits, including faster response times, stronger protection of user information, and lower long-term costs. For developers in the United States working on AI applications, local deployment is emerging as a viable alternative to cloud-based solutions.  

Why Run Llama Models Locally?  

Cloud-based AI platforms provide two main advantages through their ability to scale and their user-friendly accessibility, but they create two main problems because of their ongoing expenses and their risks to data security. Developers can run their models locally to retain full data control and eliminate usage-based costs.  

Developers can use Ollama to run Llama models locally, enabling them to test and build AI features without incurring cloud costs. Local execution delivers faster response times because it eliminates the requirement to transmit data to distant servers.  

Hardware Requirements for Mac  

Running Llama models requires users to have enough hardware resources to operate their local systems. Users can accomplish this task with modern Macs because the devices have built-in graphics processing units and share memory between all system components.  

The system requires 16GB of RAM for optimal performance, but users who need to work with larger models should choose higher RAM options. The storage space needed for a project depends on the model sizes, which range from a few gigabytes to much larger dimensions.  

Apple’s hardware ecosystem provides essential support for efficient artificial intelligence processing on user devices.  

Installing Ollama on macOS  

The installation of Ollama serves as the initial step to build a local AI environment. The platform enables users to easily download and operate the extensive language models that it provides.  

Users can download Ollama from its official website or use a package manager to install it. The program enables users to execute basic commands for model downloading and operation after its installation. The simplified setup process enables beginners to use local AI deployment systems.  

Downloading and Running Llama Models  

Developers can download Llama models via the Ollama interface after completing the installation process. The system uses commands to download models and start them for local operation.  

A standard workflow requires users to download a model first before executing it through the terminal, which allows them to use interactive sessions or API integration.  

Ollama manages the majority of challenging tasks in background operations, including both model optimization and resource management.  

Integrating Local Models into Applications  

The model becomes accessible for application integration through APIs and direct calls after it starts running on local systems. Developers can build chatbots, content-generation tools, or data-analysis systems that operate entirely on-device.  

The method proves especially valuable for applications that need to process data in real time and handle confidential information. Developers achieve better system performance through enhanced local computation while maintaining complete control over their data.  

Ollama provides interfaces that enable developers to easily integrate systems while supporting rapid development and testing.  

Performance Optimization Tips  

To achieve optimal results with local models, developers need to optimize both the hardware and software components. The process requires developers to select suitable model sizes based on their resource limitations and to handle memory management.  

Using smaller model versions delivers significant performance benefits for devices with limited computational power. Users can improve their AI performance by terminating unnecessary applications.  

The efficient hardware design of Apple systems enables users to achieve maximum efficiency in their work.  

Comparing Local vs Cloud AI  

Local AI and cloud-based AI each have their respective benefits. Cloud platforms offer scalability and powerful infrastructure, making them well-suited to handling extensive operational needs.  

The use of local AI enables users to maintain better system control, achieve faster response times, and reduce costs throughout the system’s lifecycle. Many developers find that a hybrid approach, combining local model development with cloud service expansion, delivers optimal results.  

Ollama helps developers test this balance by simplifying local deployment.  

Challenges and Limitations  

Deploying a Local AI has many advantages; however, it also introduces several limitations. System hardware limitations define the maximum size of a model that can operate successfully, as well as the ability to run complex tasks. To operate larger models, more resources are needed; these resources do not fit within the boundaries of the average consumer device. 

The process of managing updates and optimizations requires more manual work than cloud-based solutions do. Ollama continues to develop its platforms through usability enhancements and performance improvements to address existing problems.  

Conclusion: Empowering Developers with Local AI  

The ability to run Llama models on a Mac shows considerable progress toward achieving independent and efficient artificial intelligence development. Developers can create advanced applications by combining Ollama tools with Apple hardware, reducing their reliance on cloud-based systems.  

The growing need for artificial intelligence will drive local deployment as a vital development approach, as it provides developers with an effective solution that balances their needs for performance, financial resources, and system management capabilities. 

Source: ollama / ollama 

Apple Patent Signals Vision Pro Healthcare Expansion

Apple is showing its intention to develop augmented reality technology, which it plans to use for various healthcare applications through its new patent, which demonstrates how Apple Vision Pro can serve medical and business purposes. The development points to a broader strategy in which AR devices move beyond consumer experiences into high-value professional environments such as hospitals, clinics, and medical training institutions.  

The patent describes medical systems that enable users to create three-dimensional environments that display medical data and to interact with them using advanced methods. Apple is investigating spatial computing as a potential way to enhance critical healthcare operations that require precise work and rapid access to information.  

Expanding AR Beyond Consumer Use  

The primary purpose of augmented reality devices remains to provide users with tools for entertainment, communication, and general work activities. The healthcare industry within enterprise sectors provides businesses with opportunities to establish deeper relationships while developing more valuable solutions.  

The Vision Pro platform from Apple uses advanced sensors, high-resolution displays, and spatial computing technology to support professional applications. The patent suggests that these features could be leveraged to create specialized applications for medical environments.  

Apple moves into healthcare markets to connect its AR technology with industries that require precision and reliable results, thereby creating additional business opportunities and practical applications.  

Enhancing Medical Visualization  

Current AR technologies in medicine have made significant advances over traditional methods because of their ability to display detailed images to assist in diagnosing patients through X-rays, MRIs, CT scans, and 3D models used by healthcare practitioners as tools for diagnosing patient conditions. 

The Apple Vision Pro system enables users to experience data through immersive three-dimensional displays, allowing doctors to investigate body structures in greater detail. The process helps people better understand information when making choices about difficult situations.  

The AR technology in Apple’s patent will create new methods for medical professionals to engage with patient data through interactive systems that require less effort to understand.  

Real-Time Data Integration in Clinical Settings  

The patent establishes its main element by incorporating continuous data streams into the augmented reality space. The system will present essential vital signs, complete medical documentation, and operational instructions to users through their visual perspective.  

Healthcare workers would benefit from the system because it enables them to obtain essential information without interrupting their primary work. The system enables surgeons to access patient information during operations, improving their understanding of the current situation as they work.  

Apple investigates the potential of AR technology to enhance work processes by reducing the need for multiple devices and displays.  

Applications in Medical Training and Education  

The application of augmented reality technology in medical education represents a field where it will produce substantial results. Standard training methods use three main components: textbooks, simulations, and supervised practice.  

The Apple Vision Pro system enables students and trainees to participate in virtual reality simulations, which create authentic, real-world experiences. The system provides an interactive educational experience that allows users to practice skills in a safe environment, helping them develop skills and build self-confidence.  

Apple’s research into educational training tools demonstrates how augmented reality can revolutionize training in specific academic disciplines.  

Supporting Remote Collaboration and Telemedicine  

The introduction of AR technology into medical practice enables healthcare professionals to work together from distant locations. The AR system allows specialists to monitor ongoing procedures while providing remote guidance to on-site staff at the same facility.  

Telemedicine will benefit from this feature because it enables doctors to conduct more engaging and thorough patient assessments. The AR interface allows a distant expert to support surgical operations by delivering real-time operational feedback to the medical team.  

According to Apple’s patent, spatial computing will enable healthcare professionals to access expert knowledge more efficiently, thereby improving patient outcomes.  

Challenges in Healthcare Integration  

The implementation of augmented reality technology in healthcare systems faces multiple obstacles, despite its promising capabilities. The medical field demands equipment that functions with complete dependability while maintaining precise performance standards and complying with all governmental regulations.  

The safety evaluation process requires devices to undergo extensive testing, while software developers must create their products to protect confidential information with the utmost security. Healthcare staff members require training to operate these systems to their full potential.  

Apple must solve these problems before it can successfully implement augmented reality solutions in medical environments.  

From Patent to Real-World Application  

The patents present potential technical concepts that may not fully realize commercial implementation. The documents outline the areas Apple wants to pursue and provide details about its future plans.  

The healthcare focus demonstrates that AR serves two purposes as both a consumer technology and a professional and enterprise tool. Apple’s ongoing financial support of spatial computing research shows that these technologies will become increasingly important in its upcoming product innovations.  

Conclusion: AR Enters High-Value Sectors  

Apple’s patent shows that augmented reality technology now supports healthcare applications, which its Vision Pro platform offers. Through its AR development work in high-value business markets, Apple investigates how augmented reality technology delivers real value to customers beyond entertainment and workplace efficiency.  

The successful implementation of these technologies will transform medical workflows, enabling better training and improved patient care. This development marks a new stage in the progression of spatial computing technology.

Source: Trademark classification goes agentic with USPTO’s announcement of “Class ACT” assistant 

US AI Policy Updates: What Businesses Must Know (2026)

Artificial Intelligence (AI) is no longer free from regulation; as of 2026, regulatory policies are being actively established in the United States regarding AI systems and their development, deployment, and monitoring across all industries. 

Regarding AI governance, recent developments and announcements from the U.S. Department of the Treasury indicate a shift toward regulated or structured methods of governing AI, with particular emphasis in the financial sector, where large amounts of risk exposure exist. This pattern indicates a shift from AI experimentation to AI accountability. This shift toward regulations for AI systems is of significant importance to businesses, as it will impact how they can be used, scaled,, and trusted. 

Why is AI Policy Important to Businesses? 

AI systems can influence decision-making about how AI will impact a company’s customers, employees, and markets. If AI systems are not governed by regulations,, they may create bias, security vulnerabilities, and/or legal risks for businesses. 

With the increased scrutiny on the regulation of AI systems, regulators will now be focused on what constitutes a regulated AI system, whereby the AI system: 

  • It is transparent in the manner in which it makes a decision 
  • Is accountable when the outcome of the AI system results in harm or loss 
  • Has security controls to prevent manipulation and/or exploitation of the AI system 
  • Has fair processes to avoid creating discriminatory patterns. 

If a business does not meet regulatory compliance expectations, it is exposed to risks such as regulatory violations, reputational damage, business service interruptions, etc. 

AI Policy Updates in the United States Implemented in 2026 

1. Movement Towards Risk-Based Frameworks 

Moving to a risk-based framework, that is, assigning categories to AI applications based on their risk severity, is a major change in US federal regulations. 

As examples, some of the highest-risk AI applications are those that involve: 

(i) financial decision-making algorithms, 

(ii) healthcare diagnostic tools, or 

(iii) hiring/recruitment systems. 

These applications will require greater robustness in validation and monitoring, as they must comply with new,, higher safety and fairness standards. 

2. Increased Focus on Transparency 

Transparency is now a key requirement of AI systems used in the business world. Specifically, businesses must be able to describe the inner workings of their AI applications when they make automated decisions that affect individuals. 

This includes, as a minimum, the following items: 

(i) documentation of the sources of training data utilized to develop the AI application, 

(ii) providing information about the criteria utilized to make automated decisions, and 

(iii) providing notification to affected parties that an AI application was used to make the automated decision. 

Companies that have adopted “black box” or complex models as part of their AI application will need to develop new business practices to comply. 

3. Integration of AI and Cybersecurity Standards 

AI is now being treated as part of a company’s broader cybersecurity ecosystem. This means AI systems must meet the same protection standards as other digital infrastructure. 

Guidance aligned with agencies like the Cybersecurity and Infrastructure Security Agency emphasizes: 

  • Securing data pipelines used for training AI models 
  • Protecting systems from adversarial attacks 

This integration ensures that AI does not become a weak link in enterprise security. 

4. Increased Accountability for AI Outcomes 

A major policy shift in 2026 is the focus on accountability. Businesses are now responsible for the outcomes generated by their AI systems. 

Implications include: 

  • Legal liability for biased or harmful decisions 
  • Requirement for human oversight in critical processes 
  • Maintaining audit trails for AI-driven actions 

This marks a clear transition from experimental AI use to regulated deployment environments. 

5. Multi-Agency Oversight and Coordination 

AI policy is no longer managed by a single regulatory body. Multiple agencies are now involved, each addressing different aspects such as finance, national security, and consumer protection. 

This creates a more comprehensive but complex regulatory landscape. 

For businesses, it means: 

  • Navigating overlapping regulations 
  • Aligning AI systems with multiple compliance frameworks 
  • Legal liability for biased or harmful decisions 
  • Requirement for human oversight in critical processes 
  • Maintaining audit trails for AI-driven actions 

This marks a clear transition from experimental AI use to regulated deployment environments 

What Businesses Should Do Now 

Adapting to AI policy changes requires proactive planning rather than reactive fixes. Companies that integrate compliance early can avoid costly disruptions later. 

Key steps include: 

  • Conducting risk assessments for all AI systems 
  • Implementing documentation and transparency protocols 
  • Aligning AI governance with cybersecurity practices 
  • Training teams on ethical and compliant AI usage 

These actions not only ensure compliance but also improve system reliability and trust. 

Conclusion 

While it may be viewed as restrictive, regulations are actually creating a more stable and trustworthy AI ecosystem. Policies clarify uncertainty and allow for companies’ growth to happen responsively. 

Being proactive—being compliant early—is not only a commitment to legal obligations but will provide competitive edge; thus the companies who build their AI systems to be secure, transparent and reliable will benefit from early implementation of compliance measures from 2026 onwards; succeeding with AI will not only be based on innovation but on responsible innovations. 

Source: Treasury Releases Two New Resources to Guide AI Use in the Financial Sector 

Vanta vs Drata vs Secureframe: Best SOC 2 Tools (2026)

SOC 2 Compliance has become more than just a “nice-to-have” in 2026; it often distinguishes a successful closed deal from one that is lost forever. For enterprise clients, providing proof of your security posture is critical to getting contracts signed without proof of SOC 2 compliance, it’s highly likely your growth will slow or stop altogether. 

According to the American Institute of Certified Public Accountants’ SOC 2 compliance framework, you must continuously monitor your internal controls across the principles of security, availability, and confidentiality. Manually managing this process can be very resource-intensive. Therefore, the need for compliance automation tools has become critical for enterprise clients when working with startups. 

Why SOC 2 Automation Tools Matter 

Many leading compliance automation platforms, such as Vanta, Drata, and Secureframe, are designed to accommodate diverse business needs when preparing for SOC 2. 

Reasons Why SOC 2 Automation Tools are Important: 

  • The tasks required to achieve SOC 2 compliance include collecting evidence, monitoring your systems, and preparing for your audit. Unfortunately, completing each of these tasks manually can take several months, delaying SOC 2 compliance. 
  • By using automation platforms in the following manner, the time frame previously required to achieve SOC 2 compliance can be shortened significantly: 
  • Continuous monitoring of your compliance controls 
  • Automatic evidence collection in support of your audit 
  • Integration with cloud service providers and SaaS-based applications 
  • Real-time notifications if you have any compliance deficiencies 

Ultimately, using automation tools will lead to longer-term compliance management, faster compliance, and less time spent on manual work. 

Feature Vanta Drata Secureframe 
Ease of Use Very High High High 
Automation Depth Strong Very Strong Moderate 
Integrations Extensive Extensive Good 
Pricing Mid–High High Mid 
Best For Startups Scaling companies Budget-conscious teams 

Vanta: Best for Speed and Simplicity 

Vanta is widely known for making compliance approachable, especially for startups undergoing their first SOC 2 assessment. Its interface is clean, and onboarding is relatively quick compared to competitors. 

What makes Vanta stand out is how it simplifies complex workflows without overwhelming users. Teams can quickly understand what needs to be done and track progress in a centralized dashboard. 

Key strengths include: 

  • Intuitive user interface with minimal learning curve 
  • Fast SOC 2 readiness timelines 
  • Strong integrations with tools like AWS, Slack, and GitHub 

However, while Vanta excels in usability, companies with more complex environments may find its automation capabilities slightly limited compared to those of more advanced platforms. 

Drata: Best for Advanced Automation and Scale 

For companies that need deeper automation and continuous monitoring, Drata offers a more robust solution. It focuses heavily on real-time compliance tracking and detailed reporting. 

Drata is particularly useful for organizations managing multiple frameworks simultaneously, such as SOC 2, ISO 27001, and HIPAA. Its automation reduces manual intervention and ensures controls are consistently enforced. 

Where Drata leads: 

  • Real-time control monitoring and alerts 
  • Advanced reporting and audit readiness features 
  • High scalability for growing companies 

The trade-off is that Drata can feel slightly more complex during onboarding, especially for smaller teams without dedicated compliance resources. 

Secureframe: Best Value for Teams on a Budget 

Secureframe is a cost-effective solution for startups looking to achieve compliance without breaking the bank. 

Secureframe provides automated toolkits that walk through the various steps of SOC 2 requirements. This allows companies with little to no compliance experience to adopt a structured approach. 

The main benefits of Secureframe as compared to other solutions include: 

  • Low cost compared to competing solutions. 
  • Automated tool kits to walk new compliance applicants through the various steps to successfully obtain compliance. 
  • Secureframe integrates with many common SaaS tools. 
  • While Secureframe addresses all major compliance categories, it likely does not offer the scalability and automation that Drata does. 

How to Differentiate the Three Solutions That Matter 

All three platforms achieve their objectives; however, they differ in how they help an organization comply with laws and regulations. 

  • Level of Automation vs Easy to Use 

Drata provides the highest level of automation, while Vanta has the most user-friendly interface and provides the quickest turnaround time. 

  • Level of Scalability 

If you are a company looking to grow rapidly or use multiple compliance frameworks, Drata offers greater scalability. 

  • Budget Considerations 

Secureframe provides a good entry point for new companies with limited budgets. 

These differences can significantly impact your decision on what platform best fits your long-term business goals. 

How to Choose the Right Tool 

The right tool depends on your organization’s current state and the future growth direction. 

Be aware of the following: 

  •  Vanta is good if you are looking for a rapid and user-friendly way to achieve compliance. 
  •  Drata is well-suited to companies where automation and scalability are key priorities. 
  •  Secureframe is well-suited for organizations concerned about cost efficiency. 

When evaluating tools, consider both current requirements and future needs, since compliance is an ongoing requirement, not just a one-time event. 

Conclusion 

There is no single “best” SOC 2 tool; each tool will have its own merits specific to your company. Vanta is a simple, easy way to reach compliance. Drata offers the highest level of automation, while Secureframe is the most balanced in terms of functional performance and pricing. 

As compliance requirements continue indefinitely, determining the right products to help your company scale more quickly is important, as it will affect how long it takes to remain compliant after achieving certification. 

Source:The best SOC 2 compliance software for 2026