Cloud Virtual CISO
Three intriguing AI cybersecurity use cases from a Cloud Virtual CISO intriguing cybersecurity AI use cases
For years, They’ve believed artificial intelligence might transform cybersecurity and help defenders. According to Google Cloud, AI can speed up defences by automating processes that formerly required security experts to labour.
While full automation is still a long way off, AI in cybersecurity is already providing assisting skills. Today’s security operations teams can benefit from malware analysis, summarization, and natural-language searches, and AI can speed up patching.
AI malware analysis
Attackers have created new malware varieties at an astonishing rate, despite malware being one of the oldest threats. Defenders and malware analyzers have more varieties, which increases their responsibilities. Automation helps here.
Their Gemini 1.5 Pro was tested for malware analysis. They gave a simple query and code to analyse and requested it to identify dangerous files. It was also required to list compromising symptoms and activities.
Gemini 1.5 Pro’s 1 million token context window allowed it to parse malware code in a single pass and normally in 30 to 40 seconds, unlike previous foundation models that performed less accurately. Decompiled WannaCry malware code was one of the samples They tested Gemini 1.5 Pro on. The model identified the killswitch in 34 seconds in one pass.
They tested decompiled and disassembled code with Gemini 1.5 Pro on multiple malware files. Always correct, it created human-readable summaries.
The experiment report by Google and Mandiant experts stated that Gemini 1.5 Pro was able to accurately identify code that was obtaining zero detections on VirusTotal. As They improve defence outcomes, Gemini 1.5 Pro will allow a 2 million token context frame to transform malware analysis at scale.
Boosting SecOps with AI
Security operations teams use a lot of manual labour. They can utilise AI to reduce that labour, train new team members faster, and speed up process-intensive operations like threat intelligence analysis and case investigation noise summarising. Modelling security nuances is also necessary. Their security-focused AI API, SecLM, integrates models, business logic, retrieval, and grounding into a holistic solution. It accesses Google DeepMind’s cutting-edge AI and threat intelligence and security data.
Onboarding new team members is one of AI’s greatest SecOps benefits. Artificial intelligence can construct reliable search queries instead of memorising proprietary SecOps platform query languages.
Natural language inquiries using Gemini in Security Operations are helping Pfizer and Fiserv onboard new team members faster, assist analysts locate answers faster, and increase security operations programme efficiency.
Additionally, AI-generated summaries can save time by integrating threat research and explaining difficult facts in natural language. The director of information security at a leading multinational professional services organisation told Google Cloud that Gemini Threat Intelligence AI summaries can help write an overview of the threat actor, including relevant and associated entities and targeted regions.
The customer remarked the information flows well and helps us obtain intelligence quickly.
Investigation summaries can be generated by AI. As security operations centre teams manage more data, they must detect, validate, and respond to events faster. Teams can locate high-risk signals and act with natural-language searches and investigation summaries.
Security solution scaling with AI
In January, Google’s Machine Learning for Security team published a free, open-source fuzzing platform to help researchers and developers improve vulnerability-finding. The team told AI foundation models to write project-specific code to boost fuzzing coverage and uncover additional vulnerabilities. This was added to OSS-Fuzz, a free service that runs open-source fuzzers and privately alerts developers of vulnerabilities.
Success in the experiment: With AI-generated, extended fuzzing coverage, OSS-Fuzz covered over 300 projects and uncovered new vulnerabilities in two projects that had been fuzzed for years.
The team noted, “Without the completely LLM-generated code, these two vulnerabilities could have remained undiscovered and unfixed indefinitely.” They patched vulnerabilities with AI. An automated pipeline for foundation models to analyze software for vulnerabilities, develop patches, and test them before picking the best candidates for human review was created.
The potential for AI to find and patch vulnerabilities is expanding. By stacking tiny advances, well-crafted AI solutions can revolutionize security and boost productivity.
They think AI foundation models should be regulated by Their Secure AI Framework or a similar risk-management foundation to maximize effect and minimise risk.
Please contact Ask Office of the CISO or attend Their security leader events to learn more. Attend Their June 26 Security Talks event to learn more about Their AI-powered security product vision.
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