The people and institutions closest to a community understand their needs best. Libraries, small businesses, nonprofits, and schools see firsthand where AI can create opportunity and where people need support to navigate change. At Microsoft, our role is to help these trusted institutions lead by providing practical resources, training, and clear safeguards that strengthen local capacity and put communities at the center of AI literacy and adoption.
Public libraries have served as trusted centers of community learning across the United States for generations. Today, we are announcing a new initiative with the Public Library Association, a division of the American Library Association, to expand resources and no-cost AI literacy and skills training for public library workers and their patrons.
This work delivers on the commitment we made earlier this year through our Community-First AI Infrastructure initiative: to expand access to AI learning and opportunity by investing directly in local training. We’re committed to neighborhood learning through libraries, free literacy training, and certification opportunities, age-appropriate learning in schools and colleges, and support for nonprofit and small-business workforces. This new library initiative is the latest example of our broader work to help trusted local institutions serve as entry points for AI learning, skills development, and opportunity. This program comes alongside small business accelerator programs in TechSpark, our new Elevate for Changemakers platform connecting nonprofits to resources, and our ongoing national and statewide work to bring safe and responsible AI to education.
The Public Library Association AI literacy initiative we are announcing today, Building AI Skills and Agency Through Libraries, is designed to build practical skills, critical awareness, and the judgment to use AI safely and appropriately. It will pair Microsoft’s AI skilling resources with PLA’s national network of expert practitioners to support library workers and community patrons as they navigate this rapidly evolving technology.
The initiative will expand practical resources and training for library workers, create opportunities to learn from one another through a Community of Practice, and offer free webinars and guidance to support their work with patrons.
Library workers help people navigate information, find learning resources, and make informed decisions for themselves and their families. Supporting those workers is a practical way to bring AI learning closer to people, while respecting the independence of the institutions they trust.
The approach reflects a broader view of AI literacy: people need more than instructions for using AI tools; they also need the knowledge and judgment to decide whether, when and how those tools should be used. The training will help library workers and patrons understand how AI works and expand their skills, while also evaluating potential risks, protecting personal and confidential information, critically evaluating AI-generated claims and sources, and better understanding considerations related to safety and bias.
Last July, we launched Microsoft Elevate, a company-wide initiative that brings together our work to help people and institutions build the skills and capacity to navigate AI responsibly. Under the Elevate charter, each year, Microsoft supports nearly one million nonprofit organizations and education systems worldwide with technology, training, grants and discounts. Microsoft Elevate for Educators, our global community of over 100,000 teachers and school leaders, recently recognized our inaugural group of 20 Beacon Schools and School Systems that are leading examples of AI transformation in education, bringing together effective AI practices while keeping educators and students first. They are powerful examples of what happens when you combine committed educators, AI-powered technology, and intentional, no-cost staff training.
In Washington, our Elevate commitment has included a $3 million, two-year investment supporting 10 school districts and 10 community and technical colleges with grants, coaching, and communities of practice. To date, the initiative has reached 29,375 educators, with early pilots in Kennewick and Everett helping teachers explore practical uses of AI while keeping professional judgment at the center.
Skills and access must be matched by strong safeguards. In September, Microsoft joined the American Federation of Teachers to establish a national AI Safety & Privacy Standard for Schools, with enforceable protections for student and educator data, human oversight, and transparency for families. Beginning November 1, every US school district can request Microsoft to add these protections to a new or existing agreement, without waiting for renewal or renegotiation.
That same commitment extends to nonprofit staff and leaders. The Elevate for Changemakers program has already supported over 18,000 learners, helping them build practical AI skills, connect with peers, and earn recognition for turning learning into tangible outcomes for their organizations. And today we are expanding the program and introducing a new, connected experience that brings training, resources, and community together.
Together, these efforts reflect a simple principle: responsible adoption requires more than technology. Educators, nonprofit changemakers, and community leaders need time, training, trusted connections, practical support, and clear guardrails so they can decide when AI strengthens learning and, equally important, when it does not.
For nearly a decade, Microsoft has partnered with local nonprofits and small businesses across the U.S. through our TechSpark program.
Through Fellows grants, TechSpark pairs local expertise with Microsoft coaching and a national peer network to help nonprofits expand their impact in their local community and drive accelerated innovation for local small businesses. Today, Microsoft is announcing the new 2027 TechSpark Fellows across all 50 states and releasing the 2026 Fellows Impact Report showing what a locally led model can make possible.
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Over the last three years, the Fellows cumulatively report access to more than $298 million in community funding, 59,600 people trained and upskilled in AI literacy, and more than 1,500 nonprofit partners mobilized to advance their missions within their communities. These numbers tell an important story: no single organization can create lasting opportunity on its own, and local context matters.
In Virginia, Rosie Riveters Executive Director Brittany Greer is showing what it looks like to build the human capabilities that innovation requires. Through hands-on STEM experiences, students build, test, and troubleshoot to develop the critical thinking, persistence, and independent judgment they will need to use AI thoughtfully. Her work is the kind of learning these partnerships are designed to strengthen.
Community institutions know their neighbors, understand the barriers they face, and are best positioned to decide how new resources can serve them.
The thread connecting a library worker, a job seeker, a TechSpark Fellow, and a teacher is not a particular product. It is the ability to make informed decisions with support from people and institutions they trust. Our responsibility is to make that support more useful, accessible, and human-centered, while being candid about what we have yet to learn.
As we navigate the rapid pace of innovation in this AI era, our aim is not simply to bring AI into more places. It is to help people retain a stronger voice in what happens next, in their workplaces, their classrooms, and their communities.
The post Expanding AI literacy with the Public Library Association and community-based programs appeared first on Microsoft On the Issues.
We recently published a post on “tokenomics” and “denial of wallet” attacks — a new type of DDoS attack aimed at overspending AI tokens. But before security specialists begin protecting their colleagues’ AI processes from this new threat, they should remember that the work of the information security department itself can become a target for such an attack. Since security teams are now actively testing and implementing various types of AI-based automation, the threat of external interference aimed at paralyzing the defense system directly affects their own processes and technologies.
Elastic has estimated that, depending on the “agent-based SOC” architecture, triaging a single alert from a host can cost $0.69 for an ensemble of highly specialized agents or $3.42 for a general-purpose agent with 14 different skills.
However, these averages mask a huge range of variation. At any given moment, the basic processing of a simple alert (say, a thousand tokens) can turn into a cascading investigation involving processing millions of log entries, API calls, and other related data — which amounts to millions of tokens in a matter of minutes.
Elastic itself acknowledges that it uses the Claude Sonnet 4.6 model in its SOC. This immediately raises the question: what happens when the budget is exhausted or the model’s security filters are triggered? In the worst-case scenario, the following occurs: the AI SOC launches an “orchestra of agents” to investigate suspicious events that resembles lateral movement in the infrastructure. The agent calls the Anthropic API, but the department’s monthly budget has been exhausted, and the API returns errors. The investigation grinds to a halt, and people find out about it half a day later. Moreover, if the constantly changing “security filters” of a major AI provider flag the SOC’s requests as malicious, the same effect is possible even without the budget being exhausted — as demonstrated by Hugging Face’s experience investigating the breach of their infrastructure by OpenAI.
Of course, an attacker has a vested interest in such an outcome. They can directly influence the defenders’ costs by injecting malicious prompts into the data available to them (DNS TXT records, HTTP headers, code comments, filenames, and so on), counting on the fact that these prompts will be processed by information security systems. For example, in the GhostJacking attack case, WAF logs serve as the vector for prompt injection, and in academic study, another method was proposed: researchers managed to attack the LLM security guardrails themselves, causing their requests to increase latency by 148 and the number of tokens consumed by 63. In other words, the system theoretically continues to function correctly, but the latency in processing alerts and the associated costs increase significantly.
In agent-based scenarios, budget exhaustion doesn’t always manifest as an explicit error with a “request limit exceeded” message and an immediate alert to the responsible parties. Under certain configurations, “guerrilla” scenarios can arise: a request to the model times out, the system automatically switches to a cheaper model, and as a result the quality of its outputs drops, and subagents and periodic tasks stop running. Meanwhile, the security team may continue to believe for hours on end that everything is working as before.
The task of managing expenses, of course, isn’t simply a matter of “preventing overspending”. The industry has already fallen into this trap when implementing SIEM: when companies had to pay based on the volume of telemetry collected, they began to selectively — and not always effectively — limit log collection. As a result, they ended up with detection blind spots. The same situation could arise with tokens — but faster and on a larger scale. A poorly designed cost-cutting policy could compromise the depth of investigations and the quality of responses. And in the absence of such a policy, the decision to cut something will be made not by the architect or the CISO during the system design phase, but by the analyst on duty at 3am. Or it may be an automatic limiter set by the system vendor.
A few simple principles can help avoid predictable cost overruns, and better manage situations that are truly unpredictable.
Don’t feed the AI model anything that can be verified with a standard query. Everything that’s repetitive and known in advance should be handled the old-fashioned way — with rules and data queries. These can be developed using AI, but they must operate deterministically. The model should only be used where an assessment of an ambiguous situation is truly needed.
Use strict limits and aggressively alert users when they are exceeded. Limits should be combined: a limit per task, a daily limit, and so on. Alerts about limit exceedances must immediately reach both on-duty analysts and those responsible for the system as a whole.
Decide in advance what happens when a limit is reached. Whether to stop and save money — while losing some control — or to continue and pay, is a decision that must be made before an incident occurs.
Limit agents’ permissions and the set of tools available to them. The fewer actions the system has access to, the more effectively it works on a narrow task, the fewer opportunities it has to inflate costs, and the less likely it is to be “exploited” by an outsider.
Strictly scan external, untrusted data. Incoming requests, inquiries, messages, and comments, as well as various technical fields capable of containing arbitrary text (DNS records, HTTP headers, file names) can not only lead to prompt injection, but also deliberately inflate the workload. Limit their size, and monitor the load they generate.
To avoid service outages from external providers and maintain full control over your organization’s data, evaluate the possibility of using local models deployed on-premises at least for information security needs. While these models may not be state-of-the-art, their narrow specialization and fine-tuning with company data will deliver decent performance without unexpected failures.
In a more complex architecture, the use of LLMs can be divided into several levels. The first line of defense (routing, noise filtering, data enrichment) is handled by compact on-premise models. This significantly reduces budget volatility. The second level consists of a large local model that handles the substantive work: linking events, testing hypotheses, and piecing together the picture of an incident. It’s also not billed per request, but its throughput is limited; therefore, instead of budget overruns, overloads and event queues may occur. Only isolated, truly difficult cases are forwarded to advanced cloud-based models — preferably with the explicit approval of a human analyst. In this scheme, variable costs remain, but they’re limited to a small number of cases, and each one represents a conscious choice rather than a side effect of a script running overnight.
Essentially, all of this replicates the classic multi-tiered SOC model — where the first line filters, and the third line investigates — only the roles are played by models rather than people.
Companies like Google and Microsoft are finding much bigger numbers of vulnerabilities in their own products as a result of AI. But the same technology is leading to public reporting programs becoming overwhelmed by the mass submission of speculative, duplicated, or hallucinated findings.
Now, Google’s announced it has temporarily stopped accepting submissions to its open source bug bounty program, OSS VRP.
“Why is this happening? This pause is due to a significant rise in automated submissions, the vast majority of which are not valid.”
We’ve seen this happen before. In early 2026, curl ended its HackerOne bounty program after low-quality, often AI-generated submissions overwhelmed its small security team.
Intel also launched a new bug bounty program on Intigriti but there are no longer bounties available, reportedly in order to stop a flood of AI generated reports.
Bounties can be an incentive for mass-submitting behavior when generative AI lowers the cost of producing polished-looking submissions. So, it’s understandable that companies want to limit the number of incoming reports.
If most automated submissions are invalid, a temporary pause prevents Google engineers and open-source maintainers from spending disproportionate time disproving reports rather than fixing real issues.
A pause gives Google the time and chance to introduce better controls, such as mandatory proof-of-concept requirements, evidence thresholds, and rate limits without letting the current queue keep growing.
On the other hand, it may discourage legitimate researchers and make it harder for them to submit actual issues they’ve found.
It is hard to draw a line of what you will accept, because an AI-assisted report can be valid, just like a low-quality report is not necessarily AI-generated.
And as companies like Google and Microsoft have proven, AI assisted vulnerability discovery can also support real vulnerability discovery if it is coupled with validation, deduplication, and proof.
If we have learned anything it is that AI is not “breaking” vulnerability disclosure. It is exposing an older weakness in disclosure economics: the cost of filing a plausible report has fallen sharply, while the cost of proving or disproving it remains human-intensive.
The solution seems obvious. Let AI handle the submissions and only hand over those it can validate to the engineering teams. This will definitely frustrate some bug bounty hunters, because it will be like convincing a chatbot that you have a valid point and want to “talk to a human,” but hopefully it will bring down the number of less serious bug bounty hunters and create more room for those who know what they are doing.
Google has only promised an update in Q1 2027, so any redesign details would be speculative, but we do hope they find a way for responsible bug hunters to submit their findings.
From reporting threats to removing them.
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
Companies like Google and Microsoft are finding much bigger numbers of vulnerabilities in their own products as a result of AI. But the same technology is leading to public reporting programs becoming overwhelmed by the mass submission of speculative, duplicated, or hallucinated findings.
Now, Google’s announced it has temporarily stopped accepting submissions to its open source bug bounty program, OSS VRP.
“Why is this happening? This pause is due to a significant rise in automated submissions, the vast majority of which are not valid.”
We’ve seen this happen before. In early 2026, curl ended its HackerOne bounty program after low-quality, often AI-generated submissions overwhelmed its small security team.
Intel also launched a new bug bounty program on Intigriti but there are no longer bounties available, reportedly in order to stop a flood of AI generated reports.
Bounties can be an incentive for mass-submitting behavior when generative AI lowers the cost of producing polished-looking submissions. So, it’s understandable that companies want to limit the number of incoming reports.
If most automated submissions are invalid, a temporary pause prevents Google engineers and open-source maintainers from spending disproportionate time disproving reports rather than fixing real issues.
A pause gives Google the time and chance to introduce better controls, such as mandatory proof-of-concept requirements, evidence thresholds, and rate limits without letting the current queue keep growing.
On the other hand, it may discourage legitimate researchers and make it harder for them to submit actual issues they’ve found.
It is hard to draw a line of what you will accept, because an AI-assisted report can be valid, just like a low-quality report is not necessarily AI-generated.
And as companies like Google and Microsoft have proven, AI assisted vulnerability discovery can also support real vulnerability discovery if it is coupled with validation, deduplication, and proof.
If we have learned anything it is that AI is not “breaking” vulnerability disclosure. It is exposing an older weakness in disclosure economics: the cost of filing a plausible report has fallen sharply, while the cost of proving or disproving it remains human-intensive.
The solution seems obvious. Let AI handle the submissions and only hand over those it can validate to the engineering teams. This will definitely frustrate some bug bounty hunters, because it will be like convincing a chatbot that you have a valid point and want to “talk to a human,” but hopefully it will bring down the number of less serious bug bounty hunters and create more room for those who know what they are doing.
Google has only promised an update in Q1 2027, so any redesign details would be speculative, but we do hope they find a way for responsible bug hunters to submit their findings.
From reporting threats to removing them.
Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.
Google has temporarily stopped accepting product vulnerability reports through its Open Source Software Vulnerability Reward Program (OSS VRP).
The post Google Narrows Open Source Bug Bounty Amid Wave of Invalid Automated Reports appeared first on SecurityWeek.
This one is from 1809, written by Napoleon’s nephew.
The announcement comes after Trump hosted top executives of AI companies at the White House last week.
The post Trump Names National Intelligence Director Jay Clayton to Lead a New Federal AI Task Force appeared first on SecurityWeek.
In this post, we explore how shifting online sentiment and low-cost First-Person View (FPV) technology are creating an unprecedented airborne threat vector for critical data center infrastructure.
Table Of ContentsData centers have become a driving force in the modern digital economy—powering cloud services, global enterprise operations, and the explosive growth of artificial intelligence (AI). However, due to growing negative public discourse, data centers are facing a new physical threat vector: low-cost, payload-capable drones.
According to Flashpoint research, shifting public sentiment surrounding AI development, combined with the extreme accessibility of First-Person View (FPV) drone technology, is creating an unprecedented hybrid threat to physical critical infrastructure.
Here is what you need to know about this emerging threat landscape and what it means for physical security teams protecting critical assets.
Organizations tasked with protecting critical data infrastructure need to understand that this growing threat is not developing in a vacuum. Across both clearnet and Deep and Dark Web (DDW) forums, online discussions regarding data center expansion have intensified, with a significant portion bordering on hostility. Key drivers of negative sentiment include:
While much of the current online chatter currently revolves around organized protests and aspirational threats, Flashpoint analysts note a troubling uptick in rhetoric targeting corporate tech executives and data center infrastructure.
Data centers across the United States are seeing a rapid increase in physical and operational threats. Vandalism and property destruction have become common topics in illicit online spaces when discussing data centers and their impact on everyday life. Flashpoint research highlights two primary force multipliers driving this threat:
Historically, kinetic airborne strikes required specialized equipment and advanced training. Today, that barrier to entry has virtually collapsed. Rapid improvements in drone manufacturing have made payload-capable aircraft extraordinarily accessible. In today’s market, an individual can purchase an off-the-shelf system or assemble a customized drone for under $1,000 USD.
Inspiration for these tactics is also readily available; widespread footage of FPV drones operating in conflict zones like Ukraine has demonstrated to online audiences how easily and effectively low-cost aircrafts can be weaponized. Threat actors view this as a high-yield investment, especially given the capability to deploy multiple drones in quick succession.
Organized protests to stop data center development remain prevalent, and large crowds can easily overwhelm contracted security personnel, diminishing the effectiveness of a response to an aerial threat. A malicious actor could use a protest at a data center as cover to cause physical damage to the facility while security resources are spread thin. For example, on July 19, 2026, activists threw balloons filled with acetic acid at a data center construction site in Amsterdam. In its aftermath, Flashpoint analysts captured individuals online discussing the use of drones to deliver similar payloads.
Current federal regulations limit the ability to effectively deter or stop an incoming drone threat because the US Federal Aviation Administration (FAA) classifies drones as aircraft. Therefore, organizations specializing in the physical security of data centers will likely need to increase their operational capabilities and advise companies on potential hardening to deter attacks.
Traditional foot patrols and monitoring perimeter access control points will be insufficient in mitigating overhead threats. The majority of data centers are currently not equipped with the specialized Counter-Unmanned Aircraft Systems (C-UAS) equipment, specialized training, or legal authorization needed to respond effectively to airborne incursions.
Defending against aerial incursions requires moving from reactive security to proactive, intelligence-led physical protection. Physical security teams cannot afford to rely solely on ground-level surveillance when threat actors are leveraging open-source hardware and coordinating online.
Flashpoint Physical Security Intelligence (PSI) equips security teams and executive protection units with real-time visibility into emerging physical threats before they reach your perimeter:
To learn more about how Flashpoint helps safeguard critical infrastructure, executives, and high-value assets against physical and cyber threats, request a demo today.
The post Data Center Physical Security: Mitigating FPV Drone Threats appeared first on Flashpoint.
doxx.net’s new ADN platform prevents agentic misadventure while the agent is operating under the user’s authority.
The post doxx.net Raises $38 Million to Prevent AI Agent-on-the-Internet Misadventures appeared first on SecurityWeek.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.
New campaign finance disclosure data shines a light on which US political campaigns are using AI tools and how much they are spending on them.
Candidates’, parties’ and committees’ spending reveals that AI is fast becoming an essential tool of politics. The candidates themselves are quiet about how they are using the technology in their own campaigns. It’s a sensitive issue that we have been tracking closely since we started writing our book, Rewiring Democracy, which examined how AI is beginning to influence politics. A September 2025 Pew survey of Americans found that more than 70% would think less of a candidate if they used AI to help write a speech.
Itemized expenditure disclosure data from the US Federal Election Commission, dating back to 2020, reveals at least $17m in disclosed spending on AI technology vendors across 523 federal candidates and campaigns. Data from four states, California, Colorado, Massachusetts and Washington, provides a more localized picture going back to 2022.
Beginning with the AI behemoths, at least 80 federal campaigns and committees have reported spending with OpenAI since 2024. The total spending is not huge: only about $50,000 reported, skewing slightly more Republican than Democratic. The Republican National Committee is the largest overall buyer, with nearly $10,000 in reported expenses. Top individual users include the campaigns of Republicans Mike Lawler, John Kennedy and Bill Cassidy, as well as the California Democrats Ro Khanna and Ted Lieu. Most of these expenses are listed as office expenses, subscriptions to ChatGPT for staff, or research tools, rather than as specific political services. The company’s policies prohibit some political uses of their ChatGPT tool.
OpenAI’s biggest competitor, Anthropic, has rapidly built a similar level of usage, but with a different split. At least 65 candidates or committees now report paying the Claude maker in 2026, up from essentially zero in previous years, with a nearly two-to-one Democrat-to-Republican ratio. However, the largest individual user is the campaign of Tom Cotton, a Republican senator from Arkansas, who reported more than $4,000 in spend on Anthropic software in his June filing. Other major users are the Montana independent Senate candidate Seth Bodnar and Jason Knapp, who lost a Democratic House primary in Virginia, and the Democratic Alaska Senate candidate Mary Peltola.
Candidates use either Claude or ChatGPT, rarely both, according to the disclosures. Only about 12% of campaigns or committees using either tool reported expenditures to both vendors. The Democratic lean of Anthropic usage may reflect the company’s alleged liberal skew and clashes with the Trump administration.
In contrast, Elon Musk’s xAI caters to Republican interests and, accordingly, its meager usage comes almost entirely from the political right. Just seven federal and two state-level candidates or committees have reported paying xAI, a total of about $5,000, the majority of which was spent by the presidential campaign of RFK Jr in 2024, but also includes Republicans Dave McCormick and Thomas Massie.
More dollars go to the vendors specializing in political campaign applications of AI. For years, AmplifAI, which provides automated text messaging, essentially a new iteration on robocalling technology, was a dominant target of spending, soaking up $4.7m in campaign spending in the 2022 cycle alone. It was used heavily by Democratic candidates including Mark Kelly, Joe Biden, Bernie Sanders and Adam Schiff. Spending on AmplifAI, now owned by the troubled media conglomerate Triller, seems to have tapered off in the years since 2022.
The new rising Democratic solution for AI-powered text messaging is Daisychain, which has so far garnered about $300,000 in reported candidate spend in the 2026 cycle—up from only about $50,000 reported in 2024. More than half of this year’s spending comes from the Senate campaign of Democrat Abdul El-Sayed in Michigan.
The closest equivalent on the Republican side has been Campaign Nucleus, associated with former Trump campaign manager Brad Parscale. The AI-powered voter engagement tool has attracted six-figure spending from the Republican National Committee, multiple PACs aligned with Donald Trump, and five-figure investments from Mike Johnson, Kari Lake and other candidates. It is displacing the legacy Republican-serving texting vendor Prompt.io, which has retained about $375,000 in 2026 spending to date, down from more than $500,000 in the 2022 cycle. But it continues to be used: the A More Affordable California PAC sponsored by Uber has single-handedly spent more than $1m on Prompt.io in 2026. The Republican Massachusetts gubernatorial nominee Michael Minogue has been a recurring customer, as has the failed Republican California gubernatorial candidate Ché Ahn and Republican-aligned Super PAC Neighbors for a Better Colorado.
At the state level, the AI spending is smaller but growing fast. Across the four states studied, we found a total of at least $92,000 in spending confidently attributable to modern generative AI vendors since 2022. The spending is spread across at least 108 candidates and committees. The growth has been explosive; there has already been about 10 times the amount of state-level AI spending reported in 2026 as there was in all of 2024.
Much of the state spending mirrors federal patterns. Daisychain again has the highest overall spend, and OpenAI and Claude dominate among the general-purpose AI vendors. DonorAtlas—the AI-powered prospect research tool—sticks out for its usage in these states, sitting behind only Daisychain and OpenAI and buoyed up by nearly $4,000 in spending by the California Democratic party.
Even though it has dominated so much media conversation, few candidates seem to be reporting spending on AI tools designed specifically to create synthetic audio and video, also known as “deepfakes”. We found just six federal candidates or committees reporting spending on the popular AI audio generator tool from ElevenLabs, with total spending of about $1,400 led by independent candidate for Colorado’s sixth congressional district Samir Witta. The AI image generator service Midjourney has five reported federal campaign or committee users reporting about $1,600, led by Sholdon Daniels, the Republican primary runner-up in the Texas 30th district. Combined, those two firms had less than $100 in reported spend across the four states.
However, recent data from the Wesleyan Media Project shows that at least 164 political ads in this cycle have included AI-generated media, supported by at least $80m in ad spending. What this illustrates is that candidate and committee disclosure reports are just the tip of the iceberg. They don’t cover spending on AI by political consultants, media firms and other vendors hired by the campaigns or by PACs, or independent committees raising and spending money aimed at boosting candidates’ campaigns. Those entities aren’t required to disclose detailed expenditure reports, and are very likely where the bulk of campaign AI usage is happening.
Since a large fraction of all spending in the campaign cycle will happen in the final weeks leading to November, much remains to be seen about the totality of how campaigns will leverage AI and what impact its use will have on voters’ decisions.
The attacks targeted the US Department of Education and Library and Archives Canada, and researchers linked some agents to OpenAI.
The post AI Agents Aimed SQL Injection at US and Canadian Government Sites appeared first on SecurityWeek.
Fifteen years after coining the framework, John Kindervag insists zero trust still works in the AI era—if you get the implementation right.
The post Zero Trust Creator Says Model Holds Firm Against AI-Assisted Attacks appeared first on SecurityWeek.
Using an AI chatbot, assistant, or browser to speed up your work is tempting, but doing it without your employer’s knowledge can put sensitive data at risk.
You’re swamped, so you paste a long email thread into a free chatbot and ask for a summary. It works, it saves an hour, and nobody notices. But the thread may contain customer details or confidential plans you’ve just shared with an outside service.
If your employer hasn’t approved that tool or how you’re using it, that’s shadow AI.
The UK’s National Cyber Security Centre (NCSC) defines shadow AI as:
“the use of AI technology which isn’t captured in an organisation’s approved systems and processes.”
A Microsoft study published in 2025 found that 71% of UK employees surveyed said they had used AI tools at work their employer hadn’t approved. Most people aren’t doing this to cause trouble. They want to get their work done faster, and the tools are right there.
Shadow AI isn’t limited to chatbots. It can also be:
AI features are appearing in search engines, email apps, and phones. Instead of a helpful list of links, Google now tries to answer your question. Microsoft’s Copilot drafts replies to your boss before you’ve had coffee. Your phone summarizes conversations you don’t even remember having. That makes it easy to start using one without checking whether it’s approved for work.
When you enter data into a public AI tool, it leaves your company’s control. The service may keep that data or use it to improve its models, unless specific privacy controls are in place. That can lead to data breaches, lost intellectual property, and regulatory problems.
There is also a security angle. AI assistants and agents—tools that can take actions on your behalf—are complex software and can have serious vulnerabilities. If an attacker exploits one, they may gain access to the data and services the tool has.
IBM’s 2025 Cost of a Data Breach research found that one in five organizations reported a breach linked to shadow AI. Only 37% had policies to manage AI or detect shadow AI. Organizations with a lot of shadow AI paid roughly $670,000 more per breach.
In a survey of our newsletter readers, 90% of respondents said they were worried about AI using their data without consent. Company data deserves the same care as your own.
You don’t need to avoid AI altogether. Think carefully about which apps and services you use before you share data. Start by talking to your employer, then follow these steps:
If you manage a team, remember that banning AI outright tends to push the use further out of sight. Find out why people are turning to unapproved tools. Providing useful, approved options and clear rules can help staff work faster while protecting company data.
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