Emerging GenAI/LLM- Specific Security Solutions @run2obtain

Security Solutions Description LLM Firewall An LLM firewall is a security layer specifically designed to protect LLMs from unauthorized access, malicious inputs and potentially harmful outputs. This firewall monitors and filters interactions with the LLM blocking suspicious or adversarial inputs that could manipulate the model’s behavior. It also enforces predefined rules and policies ensuring that the LLM only responds to legitimate requests within the defined ethical and functional boundaries. Additionally, the LLM firewall can prevent data exfiltration and safeguard sensitive information by controlling the flow of data in and out of the model. LLM Automated Benchmarking (includes vunerability Scanning) LLM specific benchmarking tools are specialized tools designed to identify and assess security weaknesses unique to LLMs. These capabilities include detecting potential issues such as prompt injection attacks, data leakage, adversarial inputs and model biases that malicious actors could exploit. The scanner evaluates the model responses and behaviours in various scenarios, flagging vulnerabilities that traditional security tools might overlook. @run2obtain Source: https://genai.owasp.org/resource/llm-and-generative-ai-security-solutions-landscape/

Security Solutions Description LLM Guardrails LLM guardrails are protective mechanisms designed to ensure that LLMs operate within defined ethical, legal and functional boundaries. These guardrails help prevent the model from generating harmful, biased, or inappropriate content by enforcing rules, constraints, and contextual guidelines during interaction. LLM guardrails can include content filtering, ethical guidelines, adversarial input detection and user intent validation ensuring that the LLM’s outputs align with the intended use case and organizational policies. AI Security Posture Management (AI SPM) AI SPM has emerged as a new industry term promoted by vendors & analysts to capture the concept of a platform approach to security posture management for AI including LLM and GenAI systems. AI SPM focuses on the specific security needs of these advanced AI systems. Focused on the models themselves traditionally, the stated goal of this category is to cover the entire AI lifecycle from training to deployment helping to ensure models are resilient, trustworthy and compliant with industry standards. AI SPM typically provides monitoring and address vulnerabilities like data poisoning, model drift, adversarial attacks and sensitive data leakage. @run2obtain Source: https://genai.owasp.org/resource/llm-and-generative-ai-security-solutions-landscape/

A Peek into the Future of Security for GenAI (explanation on next slide) @run2obtain Source: https://www.linkedin.com/feed/update/urn:li:activity:7253757470756454402/

@run2obtain Source: https://www.linkedin.com/feed/update/urn:li:activity:7253757470756454402/ 1. Amazon Bedrock Guardrails can be optimized by getting data from the WAF and Shield to further harden the implemented safety and security filters especially against traffic that is suspected but not clarified. This is a bi-directional complementary strategy, so Bedrock Guardrails can also identify malicious IP addresses missed by WAF and dynamically send these to WAF for active blocking. 2. Multi-agent systems are becoming commonplace, and this introduces security challenges e.g., how to scale security across these agents. We could take clues from the "cattle & pets strategy" used in microservices. 3. Security for GenAI addresses GenAI specific security issues by drawing from relevant resources e.g. OWASP top LLM, MITRE ATLAS, OWASP AI Exchange, Cloud Security Alliance initiatives 4. You'd want to integrate these strategies into your existing security framework to reduce overhead and enhance productivity. This could mean integration into GuardDuty, SecurityHub or third-party vendor solutions. AI Red Teaming becomes a critical aspect of this architecture as it helps validate the security correctness and efficiency. This has to be automated, continuous and integrated into the development lifecycle. Security for GenAI

The Future is Here mitigant.io https://www.mitigant.io/en/platform/security-for-genai @run2obtain