Protect your AI applications and agents from attacks, fakes, unauthorized access, and malicious data inputs.
Control your GenAI applications and agents and assure their alignment with their business purpose.
Proactively test GenAI models, agents, and applications before attackers or users do
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Ensure your app is compliant with changing regulations around the world across industries.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Detect and prevent malicious prompts, misuse, and data leaks to ensure your conversational AI remains safe, compliant, and trustworthy.
Protect critical AI-powered applications from adversarial attacks, unauthorized access, and model exploitation across environments.
Provide enterprise-wide AI security and governance, enabling teams to innovate safely while meeting internal risk standards.
Safeguard user-facing AI products by blocking harmful content, preserving brand reputation, and maintaining policy compliance.
Secure autonomous agents against malicious instructions, data exfiltration, and regulatory violations across industries.
Ensure hosted AI services are protected from emerging threats, maintaining secure, reliable, and trusted deployments.
Bridging Frameworks to Function in AI Safety and Security - A Practical Guide
While generative AI adoption accelerates, many organizations lack the safeguards needed to deploy systems responsibly, and high-profile incidents show that misuse is already happening. ActiveFenceโs guide, Bridging Frameworks to Function in AI Safety and Security, provides practical steps to move from aspirational principles to operational safeguards.
Key takeaways:
Generative AI is deeply embedded in consumer platforms, and the risks of misuse and misalignment are expanding. In the past year, a nonprofit shut down its chatbot after it issued harmful health advice that contradicted its mission. A major technology company faced public scrutiny when its celebrity chatbot produced sexually explicit conversations with users posing as minors. These failures highlight urgent gaps in AI safety and governance.
Malicious actors are also exploiting AI for harmful purposes. Threats include synthetic exploitation, algorithmic manipulation, prompt injection (a method of tricking models into bypassing safeguards), and model jailbreaks. Each attack expands the risk surface for organizations while reducing the margin for error.
We can see that misuse and misalignment will occur. The critical question is whether organizations are prepared to detect, prevent, and respond to avoid the reputational, ethical, and legal consequences that can come with AI adoption.
Responsible AI refers to building and deploying AI in ways that prioritize safety, fairness, accountability, and transparency. Though governments are drafting regulations, industry bodies are publishing standards, and major LLM providers have issued Responsible AI frameworks, many organizations struggle to translate these principles into practice. The ActiveFence guide provides actionable steps to operationalize AI safety at scale.
Our latest guide, Bridging Frameworks to Function in AI Safety and Security, outlines practical steps to help organizations move from principles to protections. Hereโs a preview of three strategies explored in detail:
Every safeguard starts with policy. A well-defined AI safety policy sets expectations, aligns teams, and ensures consistent enforcement. The key is to treat it as living, updated continuously to reflect new threats, grey-area use cases, and regional nuances. Static policies leave gaps while adaptive ones create resilience.
Attackers evolve quickly, and the systems that last are built with that in mind. By studying how adversaries manipulate AI, partnering with researchers, and feeding those insights back into safety guardrails, organizations can prevent misuse before it becomes a crisis.
Red teaming simulates real-world attackers to uncover vulnerabilities that internal audits miss. Both structured and freestyle testing, combined with external expertise, help organizations pressure-test their systems. The real value comes when insights are translated into concrete updates, not just reports.
This resource provides a clear, actionable roadmap for operationalizing AI safety at scale. Drawing on our work with top foundation models, extensive adversarial testing, and global monitoring of evolving abuse tactics, the guide outlines six essential strategies to embed safety into AI systems from day one.
Leaders responsible for AI systems face a fast-changing threat landscape. To stay ahead, you need clear actions that can be applied from day one. Here are six areas where organizations can begin making immediate improvements:
Whether you oversee platform integrity, AI policy, or product safety, learn more about these approaches in Bridging Frameworks to Function in AI Safety and Security and keepย innovation moving forward without the risk. Download the report for more detailed breakdowns.ย
AI innovation cannot advance without robust safety infrastructure. Organizations that fail to operationalize safeguards risk reputational, ethical, and legal fallout. ActiveFenceโs guide provides a roadmap to move from principles to protection.
Bridging Frameworks to Function in AI Safety and Security - A A Practical Guide
See why AI safety teams must apply rigorous testing and training with diverse organic and synthetic datasets.
Innovation without safety can backfire. This blog breaks down how to build GenAI systems that are not only powerful, but also secure, nuanced, and truly responsible. Learn how to move from principles to practice with red-teaming, adaptive guardrails, and real-world safeguards.
In the dark corners of the web, predators are communicating to find ways to create CSAM and groom children using generative AI. This research blog explores how they do it, and what platforms can do to stop the abuse.