Invisible, autonomous and hackable: The AI agent dilemma no one saw coming - Related to one, 400m, no, hackable:, language

Invisible, autonomous and hackable: The AI agent dilemma no one saw coming

Generative AI poses interesting security questions, and as enterprises move into the agentic world, those safety issues increase.

When AI agents enter workflows, they must be able to access sensitive data and documents to do their job — making them a significant risk for many security-minded enterprises.

“The rising use of multi-agent systems will introduce new attack vectors and vulnerabilities that could be exploited if they aren’t secured properly from the start,” revealed Nicole Carignan, VP of strategic cyber AI at Darktrace. “But the impacts and harms of those vulnerabilities could be even bigger because of the increasing volume of connection points and interfaces that multi-agent systems have.”.

Why AI agents pose such a high security risk.

AI agents — or autonomous AI that executes actions on customers’ behalf — have become extremely popular in just the last few months. Ideally, they can be plugged into tedious workflows and can perform any task, from something as simple as finding information based on internal documents to making recommendations for human employees to take.

But they present an interesting problem for enterprise security professionals: They must gain access to data that makes them effective, without accidentally opening or sending private information to others. With agents doing more of the tasks human employees used to do, the question of accuracy and accountability comes into play, potentially becoming a headache for security and compliance teams.

Chris Betz, CISO of AWS, told VentureBeat that retrieval-augmented generation (RAG) and agentic use cases “are a fascinating and interesting angle” in security.

“Organizations are going to need to think about what default sharing in their organization looks like, because an agent will find through search anything that will support its mission,” expressed Betz. “And if you overshare documents, you need to be thinking about the default sharing policy in your organization.”.

Security professionals must then ask if agents should be considered digital employees or software. How much access should agents have? How should they be identified?

Gen AI has made many enterprises more aware of potential vulnerabilities, but agents could open them to even more issues.

“Attacks that we see today impacting single-agent systems, such as data poisoning, prompt injection or social engineering to influence agent behavior, could all be vulnerabilities within a multi-agent system,” noted Carignan.

Enterprises must pay attention to what agents are able to access to ensure data security remains strong.

Betz pointed out that many security issues surrounding human employee access can extend to agents. Therefore, it “comes down to making sure that people have access to the right things and only the right things.” He added that when it comes to agentic workflows with multiple steps, “each one of those stages is an opportunity” for hackers.

One answer could be issuing specific access identities to agents.

A world where models reason about problems over the course of days is “a world where we need to be thinking more around recording the identity of the agent as well as the identity of the human responsible for that agent request everywhere in our organization,” stated Jason Clinton, CISO of model provider Anthropic.

A variation of this kind of employee access and identification could be deployed to agents.

Both Betz and Clinton believe this process can prompt enterprise leaders to rethink how they provide information access to people. It could even lead organizations to overhaul their workflows.

“Using an agentic workflow actually offers you an opportunity to bound the use cases for each step along the way to the data it needs as part of the RAG, but only the data it needs,” stated Betz.

He added that agentic workflows “can help address some of those concerns about oversharing,” because companies must consider what data is being accessed to complete actions. Clinton added that in a workflow designed around a specific set of operations, “there’s no reason why step one needs to have access to the same data that step seven needs.”.

Enterprises can also look for agentic platforms that allow them to peek inside how agents work. For example, Don Schuerman, CTO of workflow automation provider Pega, showcased his organization helps ensure agentic security by telling the user what the agent is doing.

“Our platform is already being used to audit the work humans are doing, so we can also audit every step an agent is doing,” Schuerman told VentureBeat.

Pega’s newest product, AgentX, allows human individuals to toggle to a screen outlining the steps an agent undertakes. individuals can see where along the workflow timeline the agent is and get a readout of its specific actions.

Audits, timelines and identification are not perfect solutions to the security issues presented by AI agents. But as enterprises explore agents’ potential and begin to deploy them, more targeted answers could come up as AI experimentation continues.

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Nvidia helps launch AI platform for teaching American Sign Language

Nvidia has unveiled a new AI platform for teaching people how to use American Sign Language to help bridge communication gaps.

The Signs platform is creating a validated dataset for sign language learners and developers of ASL-based AI applications.

It so happens that American Sign Language is the third most prevalent language in the United States —.

but there are vastly fewer AI tools developed with ASL data than data representing the country’s most common languages, English and Spanish.

Nvidia, the American Society for Deaf Children and creative agency Hello Monday are helping close this gap with Signs, an interactive web platform built to support ASL learning and the development of accessible AI applications.

Sign language learners can access the platform’s validated library of ASL signs to expand their vocabulary with the help of a 3D avatar that demonstrates signs — and use an AI tool that analyzes webcam footage to receive real-time feedback on their signing. Signers of any skill level can contribute by signing specific words to help build an open-source video dataset for ASL.

American Sign Language is the third-most-popular language in the [website].

The dataset — which Nvidia aims to grow to 400,000 video clips representing 1,000 signed words — is being validated by fluent ASL individuals and interpreters to ensure the accuracy of each sign, resulting in a high-quality visual dictionary and teaching tool.

“Most deaf children are born to hearing parents. Giving family members accessible tools like Signs to start learning ASL early enables them to open an effective communication channel with children as young as six to eight months old,” expressed Cheri Dowling, executive director of the American Society for Deaf Children, in a statement. “And knowing that professional ASL teachers have validated all the vocabulary on the platform, people can be confident in what they’re learning.”.

Nvidia teams plan to use this dataset to further develop AI applications that break down communication barriers between the deaf and hearing communities. The data is slated to be available to the public as a resource for building accessible technologies including AI agents, digital human applications and video conferencing tools. It could also be used to enhance Signs and enable ASL platforms across the ecosystem with real-time, AI-powered support and feedback.

Whether novice or expert, volunteers can record themselves signing to contribute to the ASL dataset.

Supporting ASL education and exploring language nuance.

Nvidia is working with sign lanuage experts on its AI-based platform.

During the data collection phase, Signs already provides a powerful platform for ASL language acquisition, offering opportunities for individuals to learn and practice an initial set of 100 signs so they can more effectively communicate with friends or family members who use ASL.

“The Signs learning platform could help families with deaf children quickly search for a specific word and see how to make the corresponding sign. It’s a tool that can help support their everyday use of ASL outside of a more formal class,” Dowling expressed. “I see both kids and parents exploring it — and I think they could play with it together.”.

While Signs currently focuses on hand movements and finger positions for each sign, ASL also incorporates facial expressions and head movements to convey meaning. The team behind Signs is exploring how these non-manual signals can be tracked and integrated in future versions of the platform.

They’re also investigating how other nuances, like regional variations and slang terms, can be represented in Signs to enrich its ASL database — and working with researchers at the Rochester Institute of Technology’s Center for Accessibility and Inclusion Research to evaluate and further improve the user experience of the Signs platform for deaf and hard-of-hearing customers.

The dataset behind Signs is planned for release later this year.

Start learning or contributing with Signs at [website], and learn more about Nvidia’s trustworthy AI initiatives. Attendees of Nvidia GTC, a global AI conference taking place March 17-21 in San Jose, will be able to participate in Signs live at the event. See notice regarding software product information.

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OpenAI’s ChatGPT explodes to 400M weekly users, with GPT-5 on the way

OpenAI’s ChatGPT has surpassed 400 million weekly active customers, a milestone that underscores the business’s growing reach across both consumer and enterprise markets, .com post from Chief Operating Officer Brad Lightcap on Thursday.

The rapid expansion comes as OpenAI faces intensifying competition from rivals such as Elon Musk’s xAI and China’s DeepSeek, both of which have lately launched high-performing models aimed at disrupting OpenAI’s dominance. Despite this, OpenAI has seen significant traction in the business sector, with more than two million enterprise individuals now leveraging ChatGPT at work—doubling from September 2024.

“ChatGPT lately crossed 400M WAU, we feel very fortunate to serve 5% of the world every week,” Lightcap wrote. He also noted that usage of OpenAI’s reasoning model API has surged fivefold since the launch of its o3 Mini model, which is designed to enhance logical inference and structured problem-solving capabilities.

chatgpt lately crossed 400M WAU, we feel very fortunate to serve 5% of the world every week.

2M+ business clients now use chatgpt at work, and reasoning model API use is up 5x since o3 mini launch.

we'll bring [website] and GPT-5 to chat and the API soon, with unlimited GPT-5 for… [website] — Brad Lightcap (@bradlightcap) February 20, 2025.

AI is reshaping the workplace: 2 million businesses now rely on ChatGPT.

The surge in enterprise adoption represents a crucial validation of OpenAI’s strategy to position ChatGPT as not just a chatbot for casual queries, but as a serious productivity tool for businesses. Companies such as Morgan Stanley, Uber, and T-Mobile have integrated OpenAI’s models into their workflows, using AI to generate reports, automate customer service, and streamline decision-making.

Notably, OpenAI’s progress comes amid heightened scrutiny over the role of generative AI in business-critical applications. The organization in the recent past secured its first federal agency customer, USAID, which is deploying ChatGPT Enterprise to reduce administrative burdens and streamline partnerships, . The expansion into government contracts implies OpenAI is succeeding in navigating the regulatory hurdles that have slowed AI adoption in public-sector institutions.

At the same time, OpenAI is deepening its presence in Japan through a joint venture with SoftBank, dubbed SB OpenAI Japan. The partnership, which involves a $3 billion annual investment from SoftBank, aims to integrate OpenAI’s technology into major Japanese enterprises, with initial deployments inside SoftBank’s own ecosystem, including its semiconductor subsidiary Arm and digital payments platform PayPay.

GPT-5 is coming: OpenAI’s next leap in artificial intelligence.

Lightcap also revealed that OpenAI is preparing to launch [website] and GPT-5, with the latter set to merge the firm’s GPT and O-series models into a single, more powerful system.

“We’ll bring [website] and GPT-5 to chat and the API soon, with unlimited GPT-5 for free people (plus people can run at even higher intelligence),” he wrote.

This move signals OpenAI’s ambition to consolidate its AI offerings into a unified model that can handle both general conversational AI tasks and more specialized reasoning-based applications.

By integrating the capabilities of its flagship GPT models with the structured problem-solving of the O-series, OpenAI is betting that a one-model-to-rule-them-all approach will give it a competitive edge over rivals that are still segmenting their AI offerings.

OPENAI ROADMAP modification FOR [website] and GPT-5:

We want to do a improved job of sharing our intended roadmap, and a much improved job simplifying our product offerings.

We want AI to “just work” for you; we realize how complicated our model and product offerings have gotten.

We hate… — Sam Altman (@sama) February 12, 2025.

The timing of the GPT-5 release is particularly critical. Musk’s xAI not long ago introduced Grok-3, a model that the enterprise asserts outperforms OpenAI’s GPT-4o in certain benchmarks, including math, science, and coding. Meanwhile, DeepSeek’s rapid rise in China has added pressure on OpenAI to maintain its lead in AI sophistication and accessibility.

The AI wars: OpenAI, xAI, and DeepSeek battle for global dominance.

OpenAI’s expansion comes at a moment of fierce competition in the AI sector, with rival companies racing to secure market share in both consumer and enterprise applications.

Musk, who co-founded OpenAI before departing in 2018, has been vocal about his concerns regarding the business’s shift toward a for-profit model. The billionaire not long ago launched an unsolicited $97 billion bid to take control of OpenAI, a move that was swiftly rejected by the business’s board. OpenAI has since positioned itself as the leader in enterprise AI deployments, with Microsoft’s backing providing both financial stability and cloud infrastructure.

Meanwhile, DeepSeek has disrupted the market with low-cost, open-source AI models that have gained traction, particularly among developers wary of OpenAI’s pricing model. The Chinese firm has claimed that it trained its latest model for under $6 million—an order of magnitude lower than what OpenAI and xAI are spending on comparable systems.

What’s next for OpenAI? The future of AI in business and beyond.

OpenAI’s latest user metrics suggest that the firm is still expanding at a rapid clip despite the mounting competition. The leap from 300 million to 400 million weekly active customers in just three months indicates that demand for AI-powered tools continues to grow, with businesses increasingly integrating them into their everyday operations.

The launch of GPT-5 will be a crucial test of OpenAI’s ability to maintain its leadership in AI. If the model delivers on promises of higher reasoning capability, superior personalization, and improved efficiency, it could cement OpenAI’s position as the go-to provider for both consumer and enterprise AI applications.

However, with Musk’s xAI, DeepSeek, and Google’s Gemini models all vying for dominance, OpenAI cannot afford to slow down. The next 12 months will likely determine whether it remains the uncontested leader in generative AI, or whether a new player will disrupt the balance of power in artificial intelligence.

The firm operates in over 30 countries and plans to set up distribution hubs in key international markets.

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