In this article, we break down the major differences between fetching news data from an AI model and a dedicated News API. It’s a topic that’s been buzzing among developers lately, and a common misconception has come with it: that pulling news through an AI is quicker and easier than using a purpose-built API. It isn’t.
We debunk that misconception in detail below, comparing AI models and NewsData.io’s News API head-to-head on source coverage, data structure, freshness, cost, and more.
The Core Difference in One Line
- AI chatbot = predicts the next likely tokens based on training data (plus, sometimes, a live search call bolted on)
- News API = retrieves real, verified articles the moment publishers release them, returned as structured JSON
That’s the whole story. Everything below is what this difference costs you in practice.
The Major Difference
It helps to understand that these two tools serve different purposes and can’t compete with each other.
An AI News Chatbot is a text-prediction engine. It was trained on massive internet snapshots up to a certain time. It generates responses by predicting the most statistically significant next words. Some AI engines now have live browser surfing capabilities, which lets them fetch a handful of pages at query time. However, at its core, an AI’s “knowledge” of the news is either frozen in time or dependent on a live layer it can’t fully control.
On the other hand, a News API isn’t trying to “know” anything. It is simply a data pipeline. It continuously crawls, ingests, cleans, and structures news data from thousands of publishers as soon as it goes live. The data is then presented to developers in a machine-readable format, usually JSON. It doesn’t guess what happened; it provides what a credible source has published. The response includes metadata and links back to the source.
That distinction, prediction vs retrieval, is the root of almost every practical difference between the two approaches.
Major Differences: Numbers Side by Side
| Criteria | Fetching News via AI Chatbot | Fetching News via a News API (NewsData.io) |
| Price at scale | To match 1,000,000 article-equivalent lookups: $0.0065 per query = $6,500/month. Roughly 32x more expensive than NewsData.io’s Basic plan. | Free: 200 credits/day. Basic: $199.99/mo for 20,000 credits (~1,000,000 articles). Roughly $0.0002 per article. |
| Comprehensiveness | Typically 1–10 pages checked per query | 100,000+ sources monitored continuously, 24/7. |
| Missing image & video URLs | Responses are plain text, no thumbnail, no embedded photo, no video link. Inline images or videos are fetched very rarely. | Every article object returns dedicated image_url and video_url fields automatically, with zero extra requests. |
| Data quality & accuracy | No fact-check layer; hallucination risk on names, dates, and figures. | Genuine article data pulled directly from the publisher, with a source link on every result. |
| Reliance on prompt engineering | Precisely scoped answers (“tech news, Germany, last hour, English only”) often take several rephrased prompts, and results can vary between identical attempts since generation is probabilistic. | Fixed structured parameters (country, category, language, q, from_date) return the same filtering logic every time. No rephrasing, no guesswork. |
| Freshness / real-time capability | Minutes-to-hours delay. | Sub-second delivery via streaming (WebSocket) plans. |
| Historical data access | Not reliably available. | 6 months (Basic) up to 10 years (Corporate). |
| Traceability / auditability | No source link in most answers. And citation accuracy isn’t guaranteed. | 100% of articles include source name, timestamp, and original URL. |
| Best suited for | One-off questions, summarizing already-fetched articles. | Apps, dashboards, trading systems, research, journalism, high-volume, ongoing use. |
Why AI Chatbots Struggle With Live News
- Knowledge Cutoffs. Every language model has a training cutoff date. Anything that happened after the cutoff date simply isn’t a part of its knowledge base unless it performs a live search.
- Hallucination Risks. Language models are built to generate fluent, plausible text, not to guarantee factual accuracy. When they don’t have enough information, they can invent quotes, misattribute stories, and can blend two unrelated stories into one.
- No Systematic Source Coverage. Typically, they fetch 1–10 pages live, versus 100,000+ sources monitored continuously by a News API.
- No Structured Metadata. Even when AI is accurate, its response is usually a few paragraphs at best. There’s no timestamp field, no category tags, no sentiment score, no source ID. Basically, nothing that a developer can feed directly into a dashboard, app, or trading algorithm.
- Cost at Scale. Running large-model inference for every single news query is computationally expensive. It’s a reasonable price for a one-off question in a chat window; it’s a poor foundation for an application that needs thousands of news lookups a minute.
Why a Dedicated News API (NewsData.io) Gets It Right
- 100,000+ sources, 206+ countries, 89+ languages monitored around the clock. Not fetched on demand.
- Free tier available: 200 credits/day, no credit card required, enough to test prototypes and integration before scaling.
- Structured JSON Output. Title, source, publish time, article ID, category, sentiment score, and original URL in every response.
- Historical Archives up to 10 years on certain higher plans. Ideal for trend analysis and research.
- Real-Time Streaming via WebSocket connections, pushing new articles the instant they are published.
The Real-Time Advantage: Streaming News API
A chatbot checks for “recent” news only when prompted, and only through whatever browsing tool it has access to at the moment. NewsData.io’s Streaming News API instead holds an open connection and pushes each new article as it’s published, no repeated polling, no “let me check”.
This matters enormously for use cases like:
- Financial trading systems that need to react to market movements in seconds, not minutes.
- Newsroom monitoring tools tracking a breaking story as it develops.
- Crisis and brand-monitoring tools that can’t afford a delay between publication and detection.
- AI agents and automated workflows that need a constant, structured feed rather than a one-time snapshot.
This streaming model is the API equivalent of a live newswire ticker, compared to an AI chatbot’s “let me check what I remember or can find out about that”.
Real Example: Same Query, Two Methods
Query used: “Latest news about Cyera in USA”
Typical AI chatbot response:
This response is completely vague. It includes no article ID, no sentiment score, no title, no news content, and it isn’t in a machine-readable format. Therefore, such responses are irrelevant for developers and their applications.
Query using Prompt Engineering:
{
"status": "success",
"totalResults": 2,
"results": [
{
"article_id": "886c0bf18570ef3481820f70aae64321",
"link": "https://www.cyera.com/press-releases/cyera-completes-acquisition-of-oasis-security",
"title": "Cyera Completes Acquisition of Oasis Security",
"description": "Cyera announces it has closed its $1 billion purchase of Oasis Security, folding the startup's non-human identity management into its AI and data security platform as a unit called Cyera Identity.",
"content": null,
"keywords": [
"cyera",
"oasis security",
"acquisition",
"non-human identity",
"ai agents"
],
"creator": null,
"language": "english",
"country": [
"united states of america"
],
"category": [
"technology",
"business"
],
"datatype": "news",
"pubDate": "2026-09-03",
"pubDateTZ": "UTC",
"fetched_at": "2026-09-15 10:37:24",
"image_url": null,
"video_url": null,
"source_id": "cyera",
"source_name": "Cyera",
"source_priority": null,
"source_url": "https://www.cyera.com",
"source_icon": null,
"sentiment": "positive",
"sentiment_stats": {
"negative": 3,
"neutral": 45,
"positive": 52
},
"ai_tag": [
"mergers and acquisitions",
"cybersecurity",
"artificial intelligence"
],
"ai_region": [
"new york, united states of america"
],
"ai_org": [
"cyera",
"oasis security"
],
"ai_summary": "Cyera completed its acquisition of Oasis Security, combining data security with non-human identity governance so one platform decides what humans, machines and AI agents can access.",
"duplicate": false
},
{
"article_id": "77c602c20d87370f1c989b45acf21b08",
"link": "https://cyberprotection-magazine.com/cyera-completes-1b-acquisition-of-oasis-security-uniting-data-and-identity-for-the-agentic-enterprise",
"title": "Cyera Completes $1B Acquisition of Oasis Security, Uniting Data and Identity for the Agentic Enterprise",
"description": "Trade coverage of the closed deal notes Cyera, valued at $12 billion, now pairs its data security platform with Oasis's NHI capabilities amid a reported near-500% rise in non-human identities at Fortune 500 firms.",
"content": null,
"keywords": [
"cyera",
"oasis security",
"agentic enterprise",
"non-human identity"
],
"creator": [
"Editors Desk"
],
"language": "english",
"country": [
"united kingdom"
],
"category": [
"technology",
"business"
],
"datatype": "news",
"pubDate": "2026-09-07",
"pubDateTZ": "UTC",
"fetched_at": "2026-09-15 10:37:24",
"image_url": null,
"video_url": null,
"source_id": "cyberprotection_magazine",
"source_name": "Cyber Protection Magazine",
"source_priority": null,
"source_url": "https://cyberprotection-magazine.com",
"source_icon": null,
"sentiment": "positive",
"sentiment_stats": {
"negative": 5,
"neutral": 50,
"positive": 45
},
"ai_tag": [
"mergers and acquisitions",
"cybersecurity"
],
"ai_region": [
"new york, united states of america"
],
"ai_org": [
"cyera",
"oasis security"
],
"ai_summary": "Cyera finalized its $1 billion Oasis Security acquisition, creating a combined platform governing what every human, machine and AI agent can see and do.",
"duplicate": false
},
This result is better than the previous one but still not on par with the result you can get from a News API. Moreover, to get such results from any AI model, you need strong prompting skills since this is a very complicated task for any AI to understand.
Prompt Used to Get Such Response
Act like an expert news-research agent and structured-data engineer. Your goal is to search the live web for current, relevant news and return the results in a JSON structure that closely mimics the NewsData.io article-response format, without using or pretending to use the NewsData.io API.
Task: Search the web for news matching the user’s query and return exactly 50 distinct news articles whenever 50 sufficiently relevant articles can be found.
Step-by-step:
1. Interpret the user’s query precisely, including topic, entities, geography, timeframe, and any implied constraints.
2. Search multiple reputable and diverse news sources. Prioritize recent, verifiable reporting and use the article’s actual publication date rather than assuming the search date.
3. Deduplicate articles that report substantially the same story. Prefer the strongest or most authoritative source when multiple articles are duplicates.
4. For every selected article, extract or derive the requested fields from the source. Never invent unavailable information. Use null when a field cannot be reliably determined.
5. Return the results as a single valid JSON object containing a "results" array with up to exactly 50 article objects, followed by no commentary.
Each article object must follow this schema exactly:
{
"article_id": string,
"link": string,
"title": string,
"description": string | null,
"content": string | null,
"keywords": string[],
"creator": string[] | null,
"language": string | null,
"country": string[],
"category": string[],
"datatype": "news",
"pubDate": string | null,
"pubDateTZ": string | null,
"fetched_at": string,
"image_url": string | null,
"video_url": string | null,
"source_id": string | null,
"source_name": string | null,
"source_priority": number | null,
"source_url": string | null,
"source_icon": string | null,
"sentiment": string | null,
"sentiment_stats": {
"negative": number,
"neutral": number,
"positive": number
} | null,
"ai_tag": string[],
"ai_region": string[],
"ai_org": string[] | null,
"ai_summary": string | null,
"duplicate": boolean
}
Accuracy rules:
Do not fabricate facts, URLs, metadata, statistics, images, or article content.
Preserve source meaning when summarizing.
Ensure every "link" points to the actual article.
Use ISO-like date/time formatting consistent with the example.
"fetched_at" should represent the current retrieval time.
Sentiment statistics must sum to approximately 100 when provided.
Set "duplicate": false for articles intentionally selected after deduplication.
Validate that the final output is syntactically valid JSON with no Markdown fences or explanatory text.
If fewer than 50 reliable articles exist, return all reliable results rather than inventing additional articles.
Take a deep breath and work on this problem step-by-step.
News Query - Latest Cyera News in English.Results using NewsData.io News API for the same query (Frontend):
This is a more visually-appealing way you can use to search for news articles using NewsData.io.
Results using NewsData.io News API for the same query (in JSON):
{
"status": "success",
"totalResults": 2,
"results": [
{
"article_id": "b7b61703d7254a5452a6503540d4f73e",
"link": "https://www.openpr.com/news/4638317/generative-ai-cybersecurity-market-size-worth-usd-15-01-billion",
"title": "Generative AI Cybersecurity Market Size Worth USD 15.01 Billion by 2034 | CAGR of 21.6%",
"description": "Polaris Market Research has released a new industry study estimating the global Generative AI Cybersecurity Market at USD 2.58 billion in 2025, with the market on track to touch USD 15.01 billion by 2034 at a CAGR of 21.6% across",
"content": "Generative AI Cybersecurity Market Size Worth USD 15.01 Billion by 2034 | CAGR of 21.6% Key Market Insights • Base year value: USD 2.58 billion (2025) • 2026 market estimate: USD 3.13 billion • Projected value: USD 15.01 billion (2034) • Growth rate: CAGR of 21.6% (2026-2034) • Leading region: North America, 39.8% revenue share in 2025 • Fastest-growing region: Asia Pacific, at a 24.3% CAGR • Leading type segment: Threat Detection & Analysis, 36.7% share in 2025 • Fastest-growing technology segment: Reinforcement Learning, 25.8% CAGR • Leading end-use segment: BFSI, 31.9% share in 2025 👉Download Free Sample PDF Copy of the Report: https://www.polarismarketresearch.com/industry-analysis/generative-ai-cybersecurity-market Market Overview Demand across the market is climbing as enterprises confront an expanding attack surface and increasingly convincing AI-generated phishing, malware, and social-engineering attempts. Conventional, signature-based tools struggle to keep pace with adversaries wielding generative models of their own, pushing organizations toward adaptive, AI-native defenses capable of real-time detection, automated response, and continuous compliance tracking. Vendors are consolidating fragmented point solutions into unified platforms that weave automation, predictive analytics, and machine learning into a single line of defense. Tightening data-protection regulation and rising security budgets reinforce this trajectory, pointing to a market forecast of sustained double-digit growth through 2034. Market Dynamics • Driver: Advanced technology consolidation is pushing organizations toward unified platforms; in July 2026, Microsoft launched MAI-Cyber-1-Flash, an AI model built to automate bug detection and remediation using teams of AI agents. • Driver: Rising cyberattack volumes are accelerating adoption a CSIS-documented breach of a U.S. bank regulator exposed data tied to over 150,000 emails, underscoring the case for AI-driven detection. • Restraint: Data privacy and security concerns temper adoption, since generative AI systems require large volumes of sensitive company and customer data to function effectively. • Restraint: High implementation cost and a shortage of skilled AI-security professionals slow deployment, particularly among small and mid-sized enterprises. • Opportunity: Growing demand to secure AI models and large language models themselves against data poisoning, prompt injection, and unauthorized access is opening new business lines for vendors. • Opportunity: The rise of agentic AI within security operations centers is expanding demand for AI-powered SOC platforms that triage alerts, investigate incidents, and coordinate response autonomously. Market Segmentation The study segments the market by type, technology, end use, and region. By type, Threat Detection & Analysis led with a 36.7% share in 2025 on its ability to flag zero-day vulnerabilities, ahead of Adversarial Defense, Insider Threat Detection, and Network Security. By technology, generative adversarial networks (GANs) and deep neural networks currently anchor the market, but Reinforcement Learning is forecast to post the fastest CAGR of 25.8% given its capacity to adapt autonomously to evolving attack methods, with variational autoencoders and natural language processing rounding out the mix. By end use, BFSI held the largest share at 31.9% in 2025 on rising fraud-detection needs, ahead of healthcare, government & defense, retail and e-commerce, manufacturing, IT & telecommunications, and energy & utilities. 👉Inquire more about this Report Before Purchase: https://www.polarismarketresearch.com/industry-analysis/generative-ai-cybersecurity-market/pricing Regional Insights • North America: Largest market with a 39.8% share in 2025, underpinned by mature cybersecurity spending and early AI adoption; the U.S. alone holds a 70.21% share of the regional market. • Asia Pacific: Fastest-growing region at a 24.3% CAGR, fueled by rapid digitalization and rising cyber-risk exposure across e-commerce, finance, and telecom; China holds a 42.8% share of the regional market. • Europe: Projected to hold a 27.6% share of the global market by 2034, propelled by GDPR-driven compliance demand; Germany is expected to post a 22.4% CAGR on the back of industrial digitalization. • Latin America & Middle East and Africa: Smaller revenue bases today but drawing growing vendor attention as digital infrastructure and financing mature. Competitive Landscape Competitive intensity in the market is high, spanning cloud and platform giants, pure-play cybersecurity leaders, and AI-native specialists. Microsoft, Google, and Amazon Web Services are embedding generative AI into broader cloud security suites, while Palo Alto Networks, CrowdStrike, and SentinelOne extend established platforms through AI-focused acquisitions and partnerships. Specialists such as Darktrace, Zscaler, Cohesity, and Lakera differentiate around autonomous threat response, zero-trust operations, and LLM security respectively, with integration depth and multi-cloud reach increasingly deciding market share. Key companies profiled in the study include Abnormal Security, AWS, BigID, Cohesity, CrowdStrike, Darktrace, Google, IBM, Lakera, Microsoft, Musarubra US LLC (Trellix), Palo Alto Networks, Recorded Future, SentinelOne, Snyk, and Zscaler, among others. Latest Industry Updates • May 2026: Akamai Technologies announced a definitive agreement to acquire LayerX, aiming to unite browser-native controls with its Zero Trust and application security portfolio. • May 2026: Cisco announced its intent to acquire Israeli non-human identity and AI security startup Astrix Security to extend Zero Trust principles to agentic AI workforces. • May 2026: Cyera acquired Israeli startup Genie Security in a deal estimated at USD 50 million, adding endpoint data-leak protection tied to generative AI tool usage. • January 2026: Torq closed a USD 140 million Series D round at a USD 1.2 billion valuation to scale its agentic AI security operations platform and expand into the U.S. federal market. More Trending Latest Reports By Polaris Market Research: Coolant Distribution Unit Market: https://www.polarismarketresearch.com/industry-analysis/coolant-distribution-unit-market Fused Deposition Modeling 3D Printing Market: https://www.polarismarketresearch.com/industry-analysis/fused-deposition-modeling-3d-printing-market Biodiversity Credit Market: https://www.polarismarketresearch.com/industry-analysis/biodiversity-credit-market Exoskeleton Market: https://www.polarismarketresearch.com/industry-analysis/exoskeleton-market Contact Us Polaris Market Research & Consulting, Inc. 8 The Green Ste 19824, Dover, DE 19901, United States USA: +1 929-297-9727 APAC: +91-9591254545 Email: sales@polarismarketresearch.com Web: www.polarismarketresearch.com About Polaris Market Research & Consulting, Inc. Polaris Market Research is a global market intelligence and consulting company delivering in-depth industry reports and actionable research across healthcare, chemicals, energy, automotive, technology, and consumer goods, combining quantitative data with qualitative expertise to deliver accurate market sizing, growth forecasts, and competitive analysis. Learn more at www.polarismarketresearch.com This release was published on openPR. Permanent link to this press release: You can edit or delete your press release Generative AI Cybersecurity Market Size Worth USD 15.01 Billion by 2034 | CAGR of 21.6% here News-ID: 4638317 • Views: ... More Releases from Polaris Market Research & Consulting, Inc. Engineering Services Outsourcing Market Size Worth USD 17.22 Billion by 2034 | C ... 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"ai_summary": "Polaris Market Research forecasts the Generative AI Cybersecurity Market to grow from USD 2.58 billion in 2025 to USD 15.01 billion by 2034, with a CAGR of 21.6%. North America leads in revenue share, while Asia Pacific is the fastest-growing region. Threat Detection & Analysis is the leading segment, and BFSI is the primary end-use sector. The market is driven by the need for adaptive defenses against AI-generated threats, with vendors integrating automation and machine learning into unified platforms.",
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"article_id": "61e203f91a03bf2f20ecdef017adcbb5",
"link": "https://hackernoon.com/11-cybersecurity-ceos-getting-the-industrys-attention-in-2026",
"title": "11 Cybersecurity CEOs Getting the Industry’s Attention in 2026",
"description": "Cybersecurity’s center of gravity is shifting. Cloud infrastructure, software supply chains, AI agents, data security, and autonomous defense are creating new categories and giving a new generation of security CEOs plenty to talk about. These 11 executives stand out not necessarily because they run the largest cybersecurity companies, but because their companies and ideas are increasingly part of the industry conversation. 1. Amiram Shachar, Upwind Amiram Shachar is having a particularly consequential year. The Upwind co-founder and CEO previously founded Spot.io, which was acquired by NetApp, and now leads one of the fastest-growing names in cloud security. In September 2026, Upwind raised $300 million at a $3.8 billion valuation, more than doubling its valuation from the roughly $1.5 billion level reported earlier in the year. The latest financing underscores the growing investor interest in securing increasingly AI-driven cloud environments, while putting Shachar among the more closely watched CEOs in cybersecurity. 2. Rob Gurzeev, CyCognito Rob Gurzeev is CEO and co-founder of CyCognito , a leading attack surface management (ASM) platform. Rob has spent his early career on the attacker's side of the problem, previously leading offensive security development for private-sector organizations and intelligence agencies. Today he and his team help enterprises reduce external risk using proprietary AI-powered technologies that simulate adversary activity, from reconnaissance through the full attack chains. This puts Gurzeev at the center of the debate over how enterprises should answer advances in offensive AI, and the new classes of risk AI adoption creates. 3. Tina D’Agostin, Alcatraz AI Tina D’Agostin leads Alcatraz AI , which applies AI-powered facial authentication to physical access control. Her background spans more than 25 years in security technology and solutions, including leadership at Johnson Controls. In 2026, Alcatraz raised a $50 million Series B, while the company reported significant growth in data-center and enterprise deployments. D’Agostin was also named to the Security Industry Association’s 2026 Women in Security Forum Power 100. 4. Yotam Segev, Cyera Yotam Segev co-founded Cyera in 2021 around data security, but the company’s relevance has expanded as AI has made enterprise data even more strategically important. Cyera raised $400 million at a $9 billion valuation in January 2026 and later announced a $1 billion acquisition of Oasis Security. Segev is increasingly articulating a broader vision connecting data protection with the security of non-human identities and AI agents. 5. Kyle Hanslovan, Huntress Kyle Hanslovan, co-founder and CEO of Huntress , has become one of the more outspoken voices on the changing economics of cybercrime. A former NSA cyber operator and Air Force offensive cyber specialist, Hanslovan helped build Huntress around protecting small and midsize businesses. In 2026, Huntress research found a 1,380% increase in device-code phishing attacks during the first four months of the year, giving Hanslovan another platform to warn that automation is lowering the barriers to cybercrime. 6. Hagai Shapira, Daylight Security Hagai Shapira, co-founder and CEO of Daylight Security , is focused on one of cybersecurity’s emerging frontiers: autonomous security. Daylight describes its platform as combining autonomous AI agents with human expertise to address security operations. That positioning places Shapira in the growing conversation around agentic security and what the SOC could look like as AI takes on more operational work. 7. Rebecca Krauthamer, QuSecure Rebecca Krauthamer is co-founder and CEO of QuSecure , putting her at the intersection of cybersecurity and quantum computing. The company focuses on post-quantum cryptography and cryptographic agility, an area gaining urgency as organizations prepare for future quantum threats. In 2026, Krauthamer was named to the inaugural Quantum Security 25 and Inc.’s Female Founders 500. 8. Michael Fey, Island Michael Fey, co-founder and CEO of Island , brings a long track record in enterprise security to the company. Before co-founding Island, Fey held senior leadership roles at Symantec and other major technology companies, and he has used that experience to build Island around the enterprise browser category. The company has continued to attract attention as it expands beyond browser security into a broader vision for enterprise work. In 2026, Fey said Island had reached $200 million in revenue while continuing to grow at roughly 100% annually, and recent coverage has put the company's valuation at approximately $4.8 billion. 9. Eoin Hinchy, Tines Eoin Hinchy co-founded Tines after more than a decade working as a security operator and experiencing firsthand the repetitive workload facing security teams. Tines has evolved from a security automation company into what it describes as an intelligent workflow platform, combining AI, automation and integrations. Hinchy’s perspective is particularly relevant as companies look to automate more of the SOC without removing humans from critical decisions. 10. Gwenyth Castro, Binarly Gwenyth Castro is one of the newer CEOs on this list. In March 2026, Binarly announced that Castro had taken over as CEO, with founder Alex Matrosov moving to the board. Castro previously spent more than 15 years in cybersecurity and held senior roles at Bishop Fox. At Binarly, she is leading the company as software supply-chain security becomes increasingly important in an era of AI-generated code and increasingly complex dependencies. 11. Dan Lorenc, Chainguard Dan Lorenc, co-founder and CEO of Chainguard , has emerged as a prominent voice on software supply-chain security and the changing nature of software development. Chainguard focuses on making open-source software more trustworthy and secure, while Lorenc has increasingly discussed how AI-generated code is changing the development process. In 2025, Chainguard raised $356 million at a $3.5 billion valuation, underscoring investor interest in the category. What Comes Next The cybersecurity industry is entering a period where cloud, identity, data, software and artificial intelligence are becoming increasingly interconnected. As new threats emerge and security teams adapt, the executives shaping these markets will play an increasingly important role in determining how organizations protect their infrastructure, people and technology in the years ahead. This story was published on HackerNoon under our Business Blogging Program",
"content": "Cybersecurity’s center of gravity is shifting. Cloud infrastructure, software supply chains, AI agents, data security, and autonomous defense are creating new categories and giving a new generation of security CEOs plenty to talk about. These 11 executives stand out not necessarily because they run the largest cybersecurity companies, but because their companies and ideas are increasingly part of the industry conversation. 1. Amiram Shachar, Upwind Amiram Shachar is having a particularly consequential year. The Upwind co-founder and CEO previously founded Spot.io, which was acquired by NetApp, and now leads one of the fastest-growing names in cloud security. In September 2026, Upwind raised $300 million at a $3.8 billion valuation, more than doubling its valuation from the roughly $1.5 billion level reported earlier in the year. The latest financing underscores the growing investor interest in securing increasingly AI-driven cloud environments, while putting Shachar among the more closely watched CEOs in cybersecurity. Upwind Upwind 2. Rob Gurzeev, CyCognito Rob Gurzeev is CEO and co-founder of CyCognito, a leading attack surface management (ASM) platform. Rob has spent his early career on the attacker's side of the problem, previously leading offensive security development for private-sector organizations and intelligence agencies. Today he and his team help enterprises reduce external risk using proprietary AI-powered technologies that simulate adversary activity, from reconnaissance through the full attack chains. This puts Gurzeev at the center of the debate over how enterprises should answer advances in offensive AI, and the new classes of risk AI adoption creates. CyCognito CyCognito 3. Tina D’Agostin, Alcatraz AI Tina D’Agostin leads Alcatraz AI, which applies AI-powered facial authentication to physical access control. Her background spans more than 25 years in security technology and solutions, including leadership at Johnson Controls. In 2026, Alcatraz raised a $50 million Series B, while the company reported significant growth in data-center and enterprise deployments. D’Agostin was also named to the Security Industry Association’s 2026 Women in Security Forum Power 100. Alcatraz AI Alcatraz AI 4. Yotam Segev, Cyera Yotam Segev co-founded Cyera in 2021 around data security, but the company’s relevance has expanded as AI has made enterprise data even more strategically important. Cyera raised $400 million at a $9 billion valuation in January 2026 and later announced a $1 billion acquisition of Oasis Security. Segev is increasingly articulating a broader vision connecting data protection with the security of non-human identities and AI agents. Cyera Cyera 5. Kyle Hanslovan, Huntress Kyle Hanslovan, co-founder and CEO of Huntress, has become one of the more outspoken voices on the changing economics of cybercrime. A former NSA cyber operator and Air Force offensive cyber specialist, Hanslovan helped build Huntress around protecting small and midsize businesses. In 2026, Huntress research found a 1,380% increase in device-code phishing attacks during the first four months of the year, giving Hanslovan another platform to warn that automation is lowering the barriers to cybercrime. Huntress Huntress 6. Hagai Shapira, Daylight Security Hagai Shapira, co-founder and CEO of Daylight Security, is focused on one of cybersecurity’s emerging frontiers: autonomous security. Daylight describes its platform as combining autonomous AI agents with human expertise to address security operations. That positioning places Shapira in the growing conversation around agentic security and what the SOC could look like as AI takes on more operational work. Daylight Security Daylight Security 7. Rebecca Krauthamer, QuSecure Rebecca Krauthamer is co-founder and CEO of QuSecure, putting her at the intersection of cybersecurity and quantum computing. The company focuses on post-quantum cryptography and cryptographic agility, an area gaining urgency as organizations prepare for future quantum threats. In 2026, Krauthamer was named to the inaugural Quantum Security 25 and Inc.’s Female Founders 500. QuSecure QuSecure 8. Michael Fey, Island Michael Fey, co-founder and CEO of Island, brings a long track record in enterprise security to the company. Before co-founding Island, Fey held senior leadership roles at Symantec and other major technology companies, and he has used that experience to build Island around the enterprise browser category. The company has continued to attract attention as it expands beyond browser security into a broader vision for enterprise work. In 2026, Fey said Island had reached $200 million in revenue while continuing to grow at roughly 100% annually, and recent coverage has put the company's valuation at approximately $4.8 billion. Island Island 9. Eoin Hinchy, Tines Eoin Hinchy co-founded Tines after more than a decade working as a security operator and experiencing firsthand the repetitive workload facing security teams. Tines has evolved from a security automation company into what it describes as an intelligent workflow platform, combining AI, automation and integrations. Hinchy’s perspective is particularly relevant as companies look to automate more of the SOC without removing humans from critical decisions. Tines Tines 10. Gwenyth Castro, Binarly Gwenyth Castro is one of the newer CEOs on this list. In March 2026, Binarly announced that Castro had taken over as CEO, with founder Alex Matrosov moving to the board. Castro previously spent more than 15 years in cybersecurity and held senior roles at Bishop Fox. At Binarly, she is leading the company as software supply-chain security becomes increasingly important in an era of AI-generated code and increasingly complex dependencies. Binarly Binarly 11. Dan Lorenc, Chainguard Dan Lorenc, co-founder and CEO of Chainguard, has emerged as a prominent voice on software supply-chain security and the changing nature of software development. Chainguard focuses on making open-source software more trustworthy and secure, while Lorenc has increasingly discussed how AI-generated code is changing the development process. In 2025, Chainguard raised $356 million at a $3.5 billion valuation, underscoring investor interest in the category. Chainguard Chainguard What Comes Next The cybersecurity industry is entering a period where cloud, identity, data, software and artificial intelligence are becoming increasingly interconnected. As new threats emerge and security teams adapt, the executives shaping these markets will play an increasingly important role in determining how organizations protect their infrastructure, people and technology in the years ahead. This story was published on HackerNoon under our Business Blogging Program",
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This result is structured in machine-readable JSON and includes data type, publish date, image URL, video URL, source ID, source name, AI tags, sentiment score, and much other useful information for applications and dashboards. And doesn’t require complicated prompt engineering.
Final Verdict: You simply can’t expect a relevant response for your application from a simple prompt on any AI model. You’ll either need to be an expert Prompt Engineer or buy a higher AI plan for complicated tasks. Whereas with a dedicated News API infrastructure such as NewsData.io, you get all the relevant news data in a single query, even in the free plan.
Where AI Still Adds Value
AI isn’t obsolete for news; it’s just the wrong tool for retrieval. Its best uses are:
- Summarizing articles a News API already pulled.
- Translating structured news content.
- Tagging sentiment or entities on top of verified data.
- Generating a reader-friendly digest from raw JSON.
The API supplies the facts; AI turns facts into a more polished output. NewsData.io supports this pairing directly with AI-ready endpoints and built-in sentiment/entinty extraction.
Bottom Line
- AI: fast to talk to, unreliable for facts, no structured data, no clear source trail.
- News API: 100,000+ sources, real-time streaming, structured JSON, free tier to start, plans from $199.99/month for serious volume.
If accuracy, freshness, or scale matter, the News API wins, every time.
FAQs
Q. Can AI chatbots give me real-time news?
Only if they’re connected to a live search or browsing tool, and even then, coverage is limited compared to a dedicated News API that continuously monitors tens of thousands of sources.
Q. Why do developers prefer News APIs over AI for news data?
News APIs return clean, structured JSON data that can be plugged directly into apps, dashboards, and analytics tools. This makes it easier for most developers.
Q. What is a real-time streaming news API?
It’s an API connection (often WebSocket-based) that pushes new articles to your application the instant they’re published, instead of requiring repeated manual requests. NewsData.io offers this for use cases where every second of delay matters, such as trading or breaking-news monitoring.
Q. Is it possible to combine AI and a News API?
Yes, and this is increasingly the standard setup. A News API supplies accurate, real-time raw articles, and an AI layer summarizes, translates, or analyzes that data, giving you both accuracy and convenience.
Raghav Sharma is a content writer and media researcher at Newsdata.io, specializing in news industry analysis, media literacy, and the evolving landscape of digital journalism. With a background in English Literature and Journalism, along with a focus on fact-based reporting standards, Raghav covers topics including news API technology, editorial bias evaluation, and responsible information consumption. Raghav’s work has covered media trends across categories, including healthcare news, international journalism, and API-driven publishing. You can connect with him on LinkedIn or explore more of his writing on the Newsdata.io blog.
