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AI in Cybersecurity: A Double-Edged Sword
The industry is moving fast, and it’s being led by chatbots. ChatGPT by OpenAI and Kimi GPT by Moonshot AI are probably the most advanced conversational AI models out there.
ChatGPT vs. Kimi GPT: A Compare and Contrast
ChatGPT: The General Purpose King
It has become so common to refer to OpenAI’s ChatGPT as the most powerful modeling cell trick since producing human-like text generation, coding, and researching since being updated with the wonders of GPT-4 Turbo and multimodal capabilities (text, voice, and image recognition) and yet still considered one of the most versatile AI tools out there.
Kimi GPT: The Dance Floor Challenger
Kimi GPT—a new software from Moonshot AI—is all about speed. It has a 128k token context window for long document analysis, live browsing over 100 sites, and some features that ChatGPT doesn’t have yet.
Of course they both have their own values, but how do they stack up against each other in the world of cybersecurity?
Feature Breakdown: ChatGPT vs. Kimi GPT
Feature |
ChatGPT |
Kimi GPT |
Speed | Can slow down during peak hours | Faster response times |
Knowledge Base | Extensive, trained on massive datasets | Potentially more focused, but details unclear |
Text Generation | Highly creative and contextually aware | Efficient, but sometimes lacks nuance |
Code Generation | Strong, but accuracy varies | Excels in mathematics and coding |
Security Risks | Prompt injection, misinformation, phishing risks | Security posture unclear, but claims better misinformation handling |
Context Window | Standard token limits | 128k-token window (ideal for deep document analysis) |
Web Search | Limited real-time research capabilities | Real-time search across 100+ websites |
Availability | Free and paid tiers available | Free access, but regionally limited |
Pros and Cons of Cybersecurity
Strengths and Weaknesses of the ChatGPT
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Well-formed NLP: Good at understanding and generating human-like text for cybersecurity research.
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Multimodal opinions through text, voice, and image are good for visual threat analysis.
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Code generation: Used for penetration testing, script automation, and security research.
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Recent Web Search: ALetto print multi-step research is useful for manifesting threats.
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Response Speed Variability: If it slows down, it won’t be reliable for urgent threat analysis.
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Privacy Concerns: User data storage and protection is super opaque.
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Disinformation: AI-generated responses may contain inaccuracies that need to be fact-checked.
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Vulnerabilities in prompt injection: Susceptible to a lot of manipulation by adversaries.
Kimi GPT: Laid Bare for Strength and Weakness
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Quick: good i Large text documents of security research contained within one session.
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Alive Threat Intelligence: searching live data on 100+ websites—has a better view of in-time security.
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Potential for math and code working: maybe useful for encryption and automating security.
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Claims to be better at misinformation handling: if true, this will help with safety issues in more of the AI work.
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Very Poor Documentation: Not clear on security policies and training data sources.
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Risks of Bias: AI picks up biases from training data that may impose a baseline upon which deliberation takes place in terms of an attack.
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Security Black Box: Lacks transparent security safeguards.
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Free Model: Exploitable by opening up free access for cyberattacks and phishing campaigns.
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Risks: May inherit biases from training data, affecting security assessments.
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Security Unknowns: Lacks transparent security safeguards.
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Free Model Risks: Open access could be exploited for cyberattacks and phishing campaigns.
Cybersecurity Contribution of Such AI Chatbots
On one side, there’s a whole new set of threat options that these 2 AIs will introduce:
Data Poisoning Attacks: Inputs into an AI framework that can tamper with the resulting model to reflect malicious intent.
Prompt Injection (Use these to generate exploitable input that can extract sensitive info from internal models and can be used against said models through social engineering.) Exploits: The attacker can see how an adversarial model elicited certain hidden paths.
Phishing and Social Engineering—AI can automatically produce very convincing fake emails/messages.
Model Theft—Cybercriminals may target these AI models for malicious purposes.
How To Use AI Responsibly
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Don’t type in sensitive info into AI models.
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Double-check and verify AI-generated info.
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Use AI to supplement cybersecurity experts, not replace them.
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Detect bias or misinformation.
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Create organizations with rules around AI use.
What’s best in cyberspace?
Conversational background, multi-tasking enabled: fundamentally, this ChatGPT went heavy on research and coding.
Real-time web searching, fast answers, and large document analysis: take Kimi GPT.
ChatGPT is going to be a safe option for the cyber stewards already having rules in place with transparency around use. Kimi GPT gives real-time results and some exploratory insight into threat intelligence—maybe after a risk assessment is done.
The main thing is to be careful: although it helps with the workflow in cybersecurity, there are always human decisions to be made in cyberspace.
For security continuity, it’s vigilance—the maintenance of your cyber self is timely intelligence and info!