AI Security

As Artificial Intelligence (AI) continues to shape industries, ensuring robust AI security is paramount. The growing integration of AI in critical systems exposes them to risks like adversarial attacks, data manipulation, and model theft. Safeguarding AI systems requires implementing secure algorithms, ensuring data integrity, and protecting models from reverse engineering. Regular audits, encryption, and AI-driven threat detection can mitigate potential risks. By prioritizing AI security, businesses can maintain trust, ensure compliance, and protect sensitive operations in an increasingly AI-driven world.

How AI Chatbots Are Putting Your Banking Accounts at Risk

How AI Chatbots Are Putting Your Banking Accounts at Risk

Ravi Jain
Examines the growing security risks associated with AI chatbots in banking, highlighting how cybercriminals exploit these tools. It explains that AI chatbots can generate malicious or incorrect links for banking sites, leading users to sophisticated phishing traps enhanced by generative AI. The text also outlines why these AI-generated links cannot be trusted, citing accuracy issues and a false sense of security, particularly for smaller financial institutions. Finally, it offers essential protective measures for individuals and discusses how specialized cybersecurity firms like Technijian can help organizations defend against these evolving AI-powered threats. ... Read More
AI data security crisis infographic showing 99% of organizations with exposed sensitive data and cybersecurity threats in 2025

AI Data Breach Statistics 2025

Ravi Jain
"AI Data Security Crisis 2025," explains that while AI tools offer significant productivity gains, they also pose a substantial risk, creating the largest data security crisis in corporate history. Ninety-nine percent of organizations have sensitive data exposed to AI tools, making data breaches a certainty rather than a possibility. This vulnerability stems from AI's insatiable appetite for data and its ability to access sensitive information beyond its intended scope, leading to both human-to-machine and machine-to-machine risks. The article stresses the urgency of implementing a three-pillar strategy for AI data security: blast radius reduction, continuous monitoring and governance, and leveraging AI-powered security solutions. It also outlines a comprehensive implementation roadmap, emphasizing the need for professional technical support to assess vulnerabilities, implement tailored solutions, and provide ongoing monitoring and compliance management. The text concludes by asserting that investing in AI data security is crucial, as the cost of inaction far outweighs the investment in protective measures. ... Read More
AI security threats

How Cybercriminals Are Weaponizing Misconfigured AI Systems

Ravi Jain
"Securing AI: A Guide to Protecting Artificial Intelligence Systems," explores the escalating threats posed by cybercriminals targeting misconfigured AI systems. It details how attackers exploit vulnerabilities in AI infrastructure, such as exposed Jupyter notebooks and weak authentication, to launch sophisticated, AI-powered attacks like prompt injection and model poisoning. The guide outlines various attack vectors across Linux and Windows environments and emphasizes the long-term impact of compromised AI models. Finally, it presents comprehensive detection and prevention strategies, including infrastructure hardening, AI-specific security measures, and enterprise security frameworks, along with services offered by Technijian to address these critical security challenges. ... Read More
Bad Likert Judge

“Bad Likert Judge” – A New Technique to Jailbreak AI Using LLM Vulnerabilities

Ravi Jain
AI jailbreaking technique called "Bad Likert Judge," which exploits large language models (LLMs) by manipulating their evaluation capabilities to generate harmful content. This method leverages LLMs' long context windows, attention mechanisms, and multi-turn prompting to bypass safety filters, significantly increasing the success rate of malicious prompts. Researchers tested this technique on several LLMs, revealing vulnerabilities particularly in areas like hate speech and malware generation, although the impact is considered an edge case and not typical LLM usage. The article also proposes countermeasures such as enhanced content filtering and proactive guardrail development to mitigate these risks. ... Read More