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February 25.2025
3 Minutes Read

Generative vs. Agentic AI: Essential Insights for In-House Counsel

Agentic AI robot typing on a laptop in a futuristic setting.

Understanding the Rise of AI: Generative vs. Agentic

Artificial intelligence (AI) is no longer the futuristic concept it once was. Today, in-house counsel are finding themselves at the crossroads of two distinct types of AI: Generative AI and Agentic AI. These technologies are not just reshaping industries but also presenting complex legal and ethical challenges that demand attention. Understanding these two categories is essential for navigating today’s rapidly evolving technological landscape and for making informed decisions in legal frameworks.

Generative AI: Content at Your Fingertips

Generative AI is a creative powerhouse capable of producing new content—from text and music to images and video—by analyzing vast amounts of data. Picture software that drafts contracts for you or creates compelling marketing visuals based on a few input parameters. This technology places huge creative power at your fingertips, making repetitive tasks easier and faster. However, it retains a fundamental limitation: Generative AI operates based on its training and does not possess independent decision-making skills. It acts as a reactive tool, executing commands but lacking strategic thought.

Agentic AI: A New Era of Autonomy

On the other hand, Agentic AI takes things a step further. Unlike its generative counterpart, Agentic AI operates autonomously, making decisions based on its perception of the environment. Imagine an autonomous vehicle that not only navigates roads but assesses potential hazards in real time. This proactive behavior allows Agentic AI to tackle more complex challenges, but it also introduces significant legal concerns regarding accountability and responsibility. What happens if an autonomous AI makes a mistake? Understanding these implications is crucial for legal teams.

Legal Challenges Looming Ahead

The disparity between Generative and Agentic AI extends beyond technical differences; it encompasses a plethora of legal and ethical issues. If a Generative AI inadvertently produces plagiarized content, the accountability rests straightforwardly with those deploying the technology. However, if an Agentic AI misinterprets data, such as making incorrect medical diagnoses autonomously, the stakes rise significantly. This introduces complex questions about liability that in-house counsel must consider. Who is at fault—the developer, the company who used the technology, or both?

Navigating Compliance and Regulatory Issues

As AI technologies like these proliferate, the legal landscape is adapting. Regulatory frameworks, such as the California Consumer Privacy Act (CCPA), and emerging privacy regulations are aiming to ensure that businesses using these AI technologies comply with legal standards. Agentic AI's ability to autonomously process data raises new challenges; such systems could violate privacy laws without human oversight. Ensuring compliance and understanding these frameworks is key for in-house legal teams.

Staying Ahead of the AI Curve

For tech-savvy in-house counsel, understanding the nuances of both Generative and Agentic AI is vital to embracing innovation responsibly. It starts with evaluating the AI systems at your disposal: Are they primarily Generative or Agentic? From there, it's important to establish internal guidelines that govern how these systems can be used—a necessary step toward mitigating risks related to data privacy, bias, and accountability.

Taking Action: Your Pathway to Responsible AI Use

In-house lawyers should proactively engage with AI technologies by developing comprehensive policies. These should include clear guidelines that account for data privacy, ethical use, and risk management. Beyond policy development, fostering a collaborative approach that involves IT, compliance, and legal teams will work to create a comprehensive strategy for AI deployment that prioritizes protection and accountability.

As we examine the impact of AI on our industry, embracing both Generative and Agentic AI offers an opportunity to leverage their strengths while navigating the associated risks. Understanding what these technologies entail will not only empower legal teams but also position them as strategic resources aligned with broader organizational goals.

To improve your strategy regarding AI implementation and policy development, consider participating in workshops or consult with specialists who can help you navigate these complex decisions.

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04.02.2025

Dataminr Raises $85 Million to Boost Generative AI for Real-Time Security

Update Dataminr's Bold Move into Generative AI Dataminr, a leader in real-time AI detection, has garnered attention by raising $85 million to enhance its generative AI capabilities. This funding aims to improve the way the organization detects threats and responds to crises, tapping into the versatile applications of AI technology to protect organizations across various sectors. The Need for Real-Time Threat Detection in a Fast-Paced World In an era where information travels faster than ever, the demand for real-time insights is critical. Whether it's natural disasters, political unrest, or cybersecurity threats, organizations face an overwhelming volume of data that can be challenging to sift through. Dataminr’s technology leverages AI to process this data and provide actionable insights that can significantly alter how organizations respond to emerging challenges. Understanding Generative AI: What It Means for Crisis Response Generative AI has become a buzzword in the tech landscape, referring to algorithms capable of creating new content from existing data. In the context of threat detection, this means AI can analyze trends, simulate potential risks, and create predictive models that inform decision-makers. Dataminr's investment positions them to innovate in how businesses and governments react to crises effectively. Enhancing AI Agents and Making Them More Agentic As AI technology evolves, so does the concept of agentic AI, which focuses on creating intelligent systems that can act autonomously within defined parameters. By refining their AI agents, Dataminr aims to enable organizations to respond more proactively to crises without needing constant human oversight. Insights from Rivals: How Other AI Leaders Are Evolving Dataminr isn’t the only player in the arena. Other tech giants like Microsoft and OpenAI are also investing heavily in AI-driven solutions for cybersecurity. Microsoft, for example, has been integrating AI agents into its platforms to automate threat detection and enhance security protocols. This competition will likely drive further innovation and shape the industry's future. Future Trends in Generative AI and Cybersecurity Looking ahead, several trends are likely to emerge as generative AI matures. As AI agents become more sophisticated, we may see a shift from reactive to proactive measures in crisis management. This evolution could include predictive analytics that help identify potential crises before they occur, fundamentally changing how organizations plan for uncertainties. Potential Risks in AI Implementation Despite its advantages, the use of AI in critical areas like threat detection brings its own set of challenges. Ethical considerations regarding data privacy and accountability must be part of the discussion, as organizations use AI to navigate sensitive information. Transparency in algorithms and equipping users with an understanding of AI’s limitations will be vital in fostering trust. Why Understanding These Trends Matters For stakeholders, whether in governments or private industries, recognizing the impact of AI advancements is crucial in making informed decisions. With the growing adoption of AI applications, understanding the nuances of agentic AI and how they influence crisis response is vital for safeguarding operations against potential threats. As Dataminr pushes its capabilities further into the realm of generative AI, it is setting a precedent for what the future of threat detection and crisis management could look like. The implications of this investment extend beyond the immediate benefits, shaping the conversations around how businesses leverage AI to navigate challenges effectively.

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