DeepSeek iOS App Disables Apple’s Defenses, Sends Data to TikTok Parent - Related to tech, build, france, parent, ios
DeepSeek iOS App Disables Apple’s Defenses, Sends Data to TikTok Parent
DeepSeek has grabbed the spotlight in the AI industry as the underdog that briefly became the world’s leading app, overtaking ChatGPT AI assistant. While many see it as the Robinhood of AI, not all things are pretty about it.
A research by NowSecure, a mobile security corporation, highlights a big privacy risk in using DeepSeek’s iOS app, hinting that the Android app is no improved.
DeepSeek Clueless About Latest Security Standards.
The security assessment by NowSecure highlights glaring weaknesses in the app’s security standards for iOS consumers.
To start with, DeepSeek’s AI assistant app does not enforce the ATS (app transport security), a security feature provided by Apple to prevent insecure communications globally, for unknown reasons.
Next, the app does not encrypt the data sent to the servers controlled by ByteDance, TikTok’s parent organization. While the information does not involve personal data, an unencrypted channel can open up opportunities for a hacker.
The investigation states, “The DeepSeek iOS app sends some mobile app registration and device data over the internet without encryption. This exposes any data in the internet traffic to both passive and active attacks.”.
Andrew Hoog, the founder of NowSend, mentions more about it in the study, “An attacker with privileged access on the network (known as a Man-in-the-Middle attack) could also intercept and modify the data, impacting the integrity of the app and data.”.
Moreover, the encryption utilises the 3DES algorithm, which is now considered an insecure form of encryption.
Organisations Advised to Stop Using DeepSeek.
Considering the privacy and security risks associated with the DeepSeek iOS app, the analysis recommends not using it in your organisation until things are fixed and improved standards are in place.
As an alternative, some organisations can try self-hosting DeepSeek or using cloud services like the Azure platform to continue using it securely.
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Tech Mahindra to Set Up 3,000-Employee GCC for Goodyear in Hyderabad
Tech Mahindra is in advanced discussions to establish a global capability centre (GCC) for Ohio-based Goodyear Tire & Rubber Co in Hyderabad, Mint reported, citing sources familiar with the matter. The proposed centre, expected to employ 3,000 people, will manage Goodyear’s research and development (R&D) and IT operations.
However, Tech Mahindra did not confirm if the firm is setting up a GCC for Goodyear.
The move aligns with Tech Mahindra’s broader strategy under CEO Mohit Joshi, who took over in December 2023 after the retirement of CP Gurnani. As part of its three-year turnaround plan, Project Fortius, the business aims to boost operating margins, accelerate revenue growth, and eliminate unprofitable tail accounts.
Tech Mahindra, which reported $[website] billion in revenue for FY24, will allocate about 2% of its 150,488 employees to manage Goodyear’s GCC. It reported a [website] year-on-year (YoY) increase in net profit for the third quarter ended December 31, 2024, reaching $115 million, with a total revenue of revenue for the quarter rising to $[website] billion.
Meanwhile, Goodyear closed 2023 with $20 billion in revenue.
Indian IT firms like Tech Mahindra often facilitate GCC setups by staffing and managing these centres, receiving payments either for the workforce supplied or as a share of revenue.
In recent news, Infosys is establishing a GCC in India for Lufthansa as part of a renewed IT contract valued at nearly $300 million, . This initiative underscores the growing adoption of build-operate-transfer (BOT) models in major contract renewals.
Read: Telangana and Andhra Pradesh, the New Hotspot for IT and GCCs.
IT firms typically transition GCC operations back to the parent corporation after a few years, but it remains unclear whether Tech Mahindra and Goodyear have similar plans.
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Mistral AI Lab to Build First Data Centre in France
French artificial intelligence startup Mistral has revealed plans to invest “several billion euros” in building its first data centre in France. The enterprise aims to gain full control over data storage and processing power.
Mistral co-founder and CEO Arthur Mensch made the announcement just before the AI summit in Paris, scheduled for February 10 and 11, 2025. At the summit, world leaders and tech industry executives will discuss the future of artificial intelligence.
, Mistral’s goal is to manage the entire AI value chain, from hardware infrastructure to software.
The move aligns with France’s push to position itself as a prime data centre hub, employing its low-carbon nuclear energy and readily available development sites to attract major investors.
The French AI lab introduced its AI assistant, Le Chat, last week.
President Emmanuel Macron expressed his support on X, exclaiming, “Vive Le Chat!” The government has backed the startup with fresh contracts, striking deals with the Ministry of the Armed Forces and the public employment agency, France Travail.
Silicon Valley-based Cerebras provides computing power. Cerebras, a challenger to Nvidia in AI training, is focused on inference, delivering AI responses efficiently.
Mistral is also preparing for an initial public offering (IPO), Mensch presented earlier during an interview with Bloomberg at the World Economic Forum (WEF).
“We’re not for sale,” Mensch told Bloomberg, adding that the firm plans to open a new office in Singapore to strengthen its presence in the booming Asia-Pacific market. It also expands operations across Europe and the United States, building on its mission to compete with industry leaders like OpenAI.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Data Deepseek Disables landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- General AI capabilities
- AI-driven scientific breakthroughs
Expert Perspectives
Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."
— AI Researcher
"Organizations that develop effective AI governance frameworks will gain competitive advantage."
— Industry Analyst
"The AI talent gap remains a critical barrier to implementation for most enterprises."
— Chief AI Officer
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:
- Improved generative models
- specialized AI applications
- enhanced AI ethics frameworks
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- AI-human collaboration systems
- multimodal AI platforms
- democratized AI development
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- General AI capabilities
- AI-driven scientific breakthroughs
- new computing paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of ai tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Responsible AI driving innovation while minimizing societal disruption
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Incremental adoption with mixed societal impacts and ongoing ethical challenges
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and ethical barriers creating significant implementation challenges
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
encryption intermediate
Basic encryption process showing plaintext conversion to ciphertext via encryption keyplatform intermediate
algorithm intermediate
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