Meta reportedly had hundreds of contractors pose as minors and send suicide, sex, and drug-related prompts to chatbots from OpenAI, Google, and Character.AI. In a single testing round, more than 45,00.
Attackers are exploiting a critical flaw in Oracle E-Business Suite, CVE-2026-46817, that allows remote, unauthenticated attackers to take over Oracle Payments.
Microsoft has introduced a new Teams admin policy that allows organizers to prevent third-party bots from joining meetings without approval.
Remote support tools are always high-risk when exposed or unpatched. This one is worth checking quickly if SimpleHelp is anywhere in the environment.
https://www.helpnetsecurity.com/2026/06/30/simplehelp-vulnerability-exploited-cve-2026-48558/
India’s AI strategy should not be about replacing one dependency with another.
Chinese open-weight models may be cheaper, easier to self-host, and useful for experimentation. But the stronger long-term path is optionality.....Indian models, open standards, multi-model stacks, and clear controls around data, security, and deployment.
What you guys think??? Should India lean more on Chinese open models for now, or focus harder on building domestic AI capability???
I’m a founder building in the AI security space, focused on how companies adopt AI tools without leaking sensitive data through prompts, browser-based AI apps, copilots, internal assistants, or agent workflows......I have product and founder experience, but I’m trying to pressure-test my understanding from the practitioner side....///
For people working in security teams.... what do founders in this space often misunderstand about how security actually gets evaluated, adopted, or blocked inside companies?
Areas I’m especially interested in:
- how teams think about DLP and data leakage in AI workflows
- browser/SaaS visibility gaps
- prompt injection and agent misuse
- IAM, access control, and audit trails
- what makes a tool operationally useful vs just another dashboard
- what security teams need before trusting a new product
I’m not looking for generic startup advice. I’m more interested in the practical gaps founders miss when building for security teams.......What would you want an AI security founder to understand before trying to sell into your environment?
Polymarket confirmed hackers stole funds from some users after attackers injected malicious code through a compromised third-party vendor.
CISA has added the remote code execution flaw CVE-2026-12569 to its Known Exploited Vulnerabilities catalog.
Decentralized prediction market Polymarket has promised to fully refund users affected by a hacker attack that came to light this week.
https://www.securityweek.com/3-million-reportedly-stolen-in-polymarket-hack/
The Linux Foundation on Thursday announced a new industry effort aimed at efficiently addressing vulnerabilities in the open source software (OSS) ecosystem.
https://www.securityweek.com/linux-foundation-unveils-new-open-source-security-project-akrites/
A lot of companies are now dealing with Shadow AI.
Employees use ChatGPT, Claude, Gemini, Perplexity, coding copilots, browser extensions, AI meeting tools, writing assistants, and other AI apps before IT or security teams formally approve them.
The risk is not just that the tool is “unapproved.”
The bigger issue is what gets shared, customer data, internal documents, source code, financial information, meeting transcripts, contracts, confidential strategy, regulated or personal data.
That made me think about how companies should estimate the actual exposure from Shadow AI.
A simple Shadow AI risk estimate should probably look at:
The point is not to predict an exact shadow ai breach cost. That is almost impossible.
But even a directional estimate can help companies understand which AI workflows are low risk, which are risky, and where governance should start.
To me, the biggest mistake is treating all AI usage the same.
An employee using AI to rewrite a generic email is very different from someone uploading customer records, source code, or legal documents into an unapproved tool.
Curious how others are thinking about this.
For companies in India adopting AI quickly, should Shadow AI risk be treated mainly as a security issue, a compliance issue, or an employee education issue?
A Microsoft investigation into a ransomware case found that 2 different attackers operated simultaneously, demonstrating that modern attacks are not always isolated events and require different responses. The activity was linked to on-premises SharePoint servers that were targeted through known vulnerabilities.
https://cybernews.com/security/microsoft-ransomware-group-sharepoint-parallel-attacks/
A Microsoft investigation into a ransomware case found that 2 different attackers operated simultaneously, demonstrating that modern attacks are not always isolated events and require different responses. The activity was linked to on-premises SharePoint servers that were targeted through known vulnerabilities.
https://cybernews.com/security/microsoft-ransomware-group-sharepoint-parallel-attacks/
LastPass has notified customers that it had fallen victim to a data breach via Klue, a third-party market analysis platform integrated with Salesforce.
A recently patched vulnerability affecting Cisco’s Unified Communications Manager (Unified CM) product is being exploited in attacks, according to exploit intelligence firm Defused.
https://www.securityweek.com/hackers-exploiting-cisco-unified-cm-vulnerability/
A lot of AI startups in India are moving fast right now.
PDF chatbots, customer support bots, sales copilots, HR assistants, internal knowledge tools, coding agents, everyone wants to ship the AI feature before competitors do.
But I don’t see enough people talking about a basic question:
What data are users and employees sending into these AI systems?
Because once the feature goes live, people start pasting everything like customer details, contracts, invoices, API keys, internal docs, support tickets, financial data, HR information or private business context.....
The model may be good. The feature may work. But if sensitive data is entering prompts without checks, logging, redaction, or access control, the product is carrying a hidden risk.
This feels especially important for Indian startups because many teams are building fast with small engineering teams, third-party APIs, open-source models, and quick MVP workflows.
AI security should not start after the first leak.
So i want to know how founders/devs here are handling this?
Are you checking prompt data before it reaches the model?
Are you redacting sensitive information?
Are you logging AI interactions safely?
Are you separating internal data from customer-facing AI features?
Or is this still mostly ignored during MVP stage?