AI is becoming both an accelerant and a distraction for cybersecurity. In many respects, AI is acting as a stress test for existing security operations by exposing how difficult many organizations still find it to enforce basic controls consistently at scale.
Artificial intelligence is rapidly embedding itself into legal workflows, but much of the conversation treats all use cases as if they carry the same level of risk, even if they do not. The more useful question is not whether AI works, but where it can be safely applied and where it cannot.
AI-savvy lawyering is already something that clients are starting to demand. The technology is capable; the challenge now is cultural and organizational change.
There is a difference between deploying AI in an existing workflow and rethinking how legal work gets done. The organizations seeing more fundamental change are the ones redesigning their operating model around what the technology makes possible.
If junior attorneys are prevented from doing the hard work of rowing the oars by an entirely cost-efficient AI-model, they may lose the years of on-the-job training needed to become those experienced senior attorneys that can take the helm.
The autonomy and proactivity of AI agents will potentially unlock extraordinary efficiencies, but also may introduce new, untraversed surface area for cyberattacks. When AI systems are empowered to act, errors and compromises can cascade faster and farther than human-driven incidents.
This article is the first in a multipart series examining litigation risk in the modern technology supply chain. Here, we set the stage by looking at the role of third-party data processors, the types of disputes that commonly arise and the contract provisions that most often determine outcomes when those disputes turn into litigation.
As AI becomes embedded in everyday business and legal operations, organizations are confronting a new expectation: simply disclosing AI use is no longer enough. A critical shift is taking place in the legal industry: transparency is no longer just about disclosure; it’s about comprehension.
Clients have pushed back on what they are willing to pay for since long before anyone heard of a large language model. AI is the latest chapter in a long story about legal fees. But it introduces a wrinkle that prior tools did not.
Across practices, offices, and partner relationships, firms operate with limited visibility into where meaningful connections exist and when those connections signal real business potential. The idea of a fully captured “single view of the client” remains difficult to achieve. The question is no longer how to improve CRM adoption. It is whether the underlying model is fit for purpose.
