Updated July 2026
Artificial intelligence (AI) remains one of the fastest-growing fields globally, and academic research is working hard to keep pace with it, alongside plenty of public interest and innovative research discussions happening well beyond academic circles too. One of the publications tracking this space is the American Journal of Artificial Intelligence (AJAI), an international, peer-reviewed journal (ISSN Online: 2639-9733, ISSN Print: 2639-9717) published by Science Publishing Group. AJAI publishes original research covering machine learning, AI ethics, healthcare applications, and related topics, with new issues released twice a year.
At TechInGot, we like connecting research trends like these to the practical career paths and questions people are actually searching for — including some of AI’s fastest-growing job categories and the ethical debates shaping how AI gets built and regulated.
A Quick, Accurate Note on AJAI’s Volume History
To keep this guide accurate: AJAI’s Volume 6 was published back in 2022, as part of the journal’s regular twice-yearly publishing schedule. By 2026, the journal has moved well past that, with newer volumes covering fresh research in areas like machine learning applications, workplace AI tools, and data science methods. If you want to explore the journal’s actual current research, its official archive is the most reliable place to look, rather than any secondhand summary — including this one.
What follows below isn’t a summary of a specific AJAI volume, but a broader, accurately sourced look at some of the same general themes AI research and industry commentary have been focused on recently: AI in law, AI security careers, and how AI systems get planned and regulated.
AI and the Legal World: The Rise of AI-Assisted Legal Work
AI in law is no longer science fiction. Law firms increasingly use AI-powered tools to review contracts, search case law, summarize depositions, and flag risks in legal documents faster than manual review alone. Some tools go further, helping predict likely case outcomes based on historical data patterns using machine learning techniques.
Fully automated courtrooms remain far off, but AI is quietly becoming a normal part of legal teams’ toolkits — handling research and document review so human lawyers can focus on judgment calls, negotiation, and courtroom strategy. This raises real, ongoing questions about fairness, privacy, and how much automation is appropriate in a system built around human judgment.
AI Security Careers: What “AI Red Team” Jobs Actually Involve
Cybersecurity is one of AI’s fastest-growing intersections, and AI red team jobs are a big part of that. These roles involve deliberately attacking AI systems — testing for weaknesses like prompt injection, data poisoning, and model extraction — before real attackers can exploit them.
This isn’t a niche curiosity anymore. Major tech companies including Microsoft, Google, NVIDIA, and OpenAI now run dedicated AI red teams, and demand has spread to AI security startups, defense contractors, and financial services firms as well. Microsoft’s own AI red team, for example, has publicly documented testing over 100 generative AI products and shared its methodology, drawing on an interdisciplinary mix of cybersecurity experts, linguists, and even neuroscientists to think through how models can be broken.
Shared frameworks like MITRE ATLAS and the OWASP Top 10 for Large Language Model Applications now give red teamers a common language for describing AI-specific threats — much like traditional cybersecurity has long relied on frameworks such as the NIST Cybersecurity Framework. As with any security role, protecting sensitive systems responsibly also means following solid baseline practices — our cybersecurity basics guide covers those fundamentals in plain language.
Salaries in this space have climbed quickly alongside demand, with experienced AI red teamers commonly earning well into six figures, and specialized contractor rates running even higher for senior, project-based work.
Which Is Easier: Cybersecurity or Artificial Intelligence?
This question comes up constantly in forums and classrooms, and honestly, there’s no single right answer — it depends heavily on someone’s background and interests.
Cybersecurity work tends to lean on systems knowledge, networking, and ethical hacking skills. AI work leans more on mathematics, data processing, and programming, particularly around machine learning models. Both fields are genuinely difficult in their own way, and the “easier” path really comes down to which skill set someone already has a head start in.
What’s increasingly clear, though, is that the two fields are converging rather than staying separate. Modern cybersecurity tools now use AI to detect threats faster than manual analysis ever could, and — as covered above — AI systems themselves increasingly need dedicated security testing. Realistically, many careers in this space now require at least some working knowledge of both.
Planning in Artificial Intelligence: Smarter Decisions for Complex Systems
Planning is a core part of how AI systems operate — it’s what lets a machine work out the best sequence of actions to reach a goal, whether that’s a delivery robot choosing a route or a spacecraft adjusting its trajectory in real time.
This research area shows up in some very practical places. Urban traffic systems increasingly use AI-driven planning to reduce congestion and cut energy waste. Robotics researchers train machines to plan tasks dynamically based on live sensor data rather than a fixed script. These use cases show how deep “planning in artificial intelligence” actually runs, well beyond the more visible chatbot and image-generation applications most people associate with AI today.
Careers Shaping the AI World in 2026
Two career paths stand out clearly right now:
- AI Red Team / AI Security Roles — ethical hacking-style roles focused on finding weaknesses in AI-powered systems before real attackers do. Demand is growing fast across banking, healthcare technology, and national defense sectors.
- AI-Focused Legal Roles — lawyers and legal-tech specialists who help clients navigate data privacy, AI-related contracts, and emerging automation regulations, a niche that’s growing as more governments introduce AI-specific legislation.
For students and job seekers, both paths reward a mix of technical literacy and domain expertise — pure technical skill alone isn’t usually enough in either field.
AI and Ethics: An Ongoing, Unresolved Conversation
Ethics remains one of the most actively debated areas in AI research, and for good reason. Academic and industry work alike keeps circling back to a few consistent themes: transparency in how AI systems make decisions, fairness and bias in the data models are trained on, and how courts and governments should realistically regulate AI tools — including the AI-assisted legal work and red-team security operations covered above.
Bias in algorithms remains a persistent, well-documented concern, largely because models tend to reflect the patterns and imbalances already present in their training data. Addressing this properly usually requires deliberate effort — diverse datasets, ongoing audits, and genuine accountability — rather than assuming a system is neutral by default.
Final Thoughts
AI research and AI-driven careers keep evolving together, whether the topic is legal tech, security, urban planning, or algorithmic fairness. Publications like the American Journal of Artificial Intelligence contribute real, peer-reviewed research to this space, even if — as covered honestly above — its actual volume history doesn’t line up with some of the more exaggerated claims that circulate online about specific issues.
For anyone genuinely curious about where AI is heading — whether through a legal career, a security-focused red team role, or simply following the research — the smartest first step is going straight to primary, verifiable sources rather than secondhand summaries, this one included.