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AI Deep Dive Part 1: The History of AI

Arete Analysis

Cyber Threats

Artificial intelligence (AI) is a subset of computer science that focuses on creating systems that can replicate human intelligence and problem-solving capabilities. This is accomplished by feeding large amounts of data into machine learning models (MLMs) and processing the data. The result is technology that can simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy.

While often seen as new, cutting-edge technology, AI has been around far longer than most would think. While the concept of AI goes back to ancient philosophers theorizing on life and death, AI as we know it began in the early 1900s. The conception of what AI is began to be portrayed in science fiction by various authors and artists throughout the early 1900s prior to what is commonly known as “the birth of AI.”

AI Through the Ages

  • The Birth of AI: 1950 – 1956

Computer scientists such as Alan Turing, Arthur Samuel, and John McCarthy set the stage for the beginning of AI. Turing published “Computer Machinery and Intelligence,” which annotated a test of machine intelligence called the Imitation Game. Turing theorized that any machine able to fool a human judge would be classified as artificial intelligence.

  • AI Maturation: 1957 – 1979

The next twenty years showed little growth for AI at a technical level. While the concept of AI became popular in pop culture, funding-backed research was minimal during this period. However, that is not to say that strides towards what AI is today were not made. The first programming languages were created, paving the way for future development. The first AI chatbot was created, which adopted a new approach to AI that we now call deep learning, and the first examples of an autonomous vehicle were created.

  • AI Boom: 1980 – 1987

During the seven-year period known as the AI boom, government funding and associated research significantly increased. The first Association for the Advancement of Artificial Intelligence (AAAI) conference was held at Sanford, and the first driverless car demonstrated its ability to drive up to 55 mph on empty roads.

  • AI Winter: 1987 – 1993

Overall, funding and interest in AI decreased during this period, leading to fewer advancements in the technology than in years prior.

  • AI agents: 1993 – 2011

Despite the initial lack of investment in AI, the technology as a whole significantly increased its capabilities during this time period. Most notably, this is when AI began being integrated into people’s daily lives with items such as the Roomba and the release of Apple’s virtual assistant, Siri.

  • Early Generative Artificial Intelligence: 2012 – Present

This brings us up to the current state of AI. The last decade has shown impressive leaps in AI’s ability to aid humans in day-to-day functions. This is also accompanied by enormous data collection from well-known companies that are able to train their AI models, which has led to the release of consumer-facing AI models such as ChatGPT, Copilot, and more.

Conclusion

AI as a whole is a fast-changing, fluid concept. Organizations regularly unveil new capabilities and breakthroughs. This was especially evident in the recent unveiling of Deepseek and the subsequent data privacy concerns. In a single day, this overturned the sector in one fell swoop. AI will likely remain a constantly changing field in the near term.

What’s Next?

Part 2 of Arete’s AI Deep Dive will examine the risks and benefits of organizations adopting AI into their business models

Sources

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Adversarial AI Evolves from Prompting to Autonomous Attack Workflows

Cybercriminals and state-linked threat actors are increasingly incorporating artificial intelligence (AI) into multiple stages of offensive operations. Rather than relying on AI only for content generation or research, threat actors are now consistently using it to support reconnaissance, vulnerability discovery, credential theft, malware development, social engineering, and activities following initial compromise. The growing use of automated and agent-based workflows is allowing adversaries to coordinate offensive malicious activities with less manual involvement, potentially increasing the speed, scale, and efficiency of future campaigns.

What’s Notable and Unique

  •  In one observed campaign, threat actors used AI to plan, build, and conduct an automated credential-harvesting operation within just several hours, demonstrating how compromised infrastructure can be rapidly repurposed to support larger-scale malicious activity.

  • Arete has observed several threat groups using AI-assisted capabilities to facilitate the analysis of data obtained during exfiltration so far this year, including INC Ransom, FulcrumSec, and Settra.

  • Attackers are also attempting to reproduce proprietary AI capabilities. Large-scale automated prompting campaigns have been used to extract information about the behavior and capabilities of proprietary AI models. The use of numerous accounts and intermediary infrastructure can make these activities more difficult to identify and attribute.

Analyst Comments

AI is increasingly becoming an operational component in the threat landscape rather than simply a tool that improves attacker productivity. By connecting reconnaissance, development, credential operations, and other activities through automated workflows, threat actors can conduct campaigns more quickly and reduce reliance on continuous human input. This trend also impacts security requirements for organizations deploying AI technologies. AI models, development environments, cloud resources, identities, repositories, and automated workflows should increasingly be considered interconnected elements of the enterprise attack surface, and organizations should maintain visibility across these areas and ensure that security controls account for both conventional threats and AI-enabled attack techniques.

Sources

  • GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI

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Top 9 Security Controls for SMBs

In today’s complex cyber threat landscape, small and medium-sized businesses (SMBs) should implement a layered, multi-pronged approach to security. Here are some proven, effective controls to consider:

1.        Implement MFA for All Users
Multi-factor authentication (MFA) is an effective and accessible way to protect sensitive data, comply with regulatory standards, and prevent unauthorized access to critical systems. By ensuring that users not only use a password but also a secure token, an organization can significantly reduce phishing attempts, credential stuffing attacks, and ransomware incidents. MFA should be enabled for email, VPN, and critical system access.

2.        Update Patch Management Programs
A modern patching program should include policies and mechanisms to manage software updates in a timely manner. Patching efforts should address not just operating system updates, but also commonly utilized software within the environment. An active patching plan aims to reduce the mean time to patch and provide metrics on existing patch efforts. In the event of a high-severity vulnerability, a mature patching program should also be able to identify the risks and exposure for a given set of systems based on the average time to patch. This helps stakeholders make informed decisions when a new exploit is released.

3.        Leverage a Properly Managed Endpoint Detection and Response (EDR) Tool
With real-time threat detection and advanced visibility, endpoint detection and response (EDR) solutions can enhance an organization’s ability to detect and investigate potential malicious activity, including ransomware, lateral movement, fileless attacks, and more. Effective management of EDR should include proper configuration, regular agent updates, and established procedures for responding to alerts.

4.        Enhance EDR with Frontline Incident Intelligence
While EDR solutions are an important component of a security program, threat actors are continually adapting their techniques to evade detection, and as a result, standard detection methods may not always identify emerging threats or novel attack behaviors. To strengthen protection, organizations can supplement EDR tools with custom detection rules informed by incident response data. These rules can help identify indicators of compromise observed during cyber incidents, providing an additional layer of visibility beyond traditional detections.

Arete Bloktd℠ illustrates this approach by transforming threat intelligence and malware analysis into curated detection rules for EDR tools. Applying frontline intelligence to security tools can result in earlier detection, less investigative burden, and a more confident response. 

5.        Create and Continuously Test an Incident Response (IR) Plan
An incident response plan can help an organization effectively identify, respond to, and recover from a cybersecurity incident while minimizing disruption and downtime. An IR plan defines clear roles, escalation paths, communication strategies, and decision frameworks for both internal and external teams. To be effective, these plans should be regularly tested with post-incident reviews or tabletop exercises involving key stakeholders.

6.        Maintain Encrypted and Tested Data Backups
In the case of a cyberattack, backups can help organizations maintain business continuity, support ransom negotiations with threat actors, and contribute to a timely and successful recovery. Encrypting backups makes it more difficult for threat actors to access or alter them, and regular testing ensures they remain accessible during a security incident.

7.        Conduct Employee Security Awareness Training
Human error is a leading cause of cybersecurity incidents, and an organization’s employees are its first line of defense against cyber threats. Security awareness training reinforces the importance of data confidentiality, supports regulatory compliance, and helps employees recognize and understand red flags such as suspicious emails, malicious links, and social engineering attempts.

8.        Assess and Address Existing Risk and Vulnerabilities
To maintain a proactive approach to risk management and improve resilience, organizations should regularly assess existing vulnerabilities through expert-led Vulnerability Assessments, Penetration Testing, and Threat Hunting Assessments. These exercises can help identify security gaps, support regulatory compliance, and prevent potential incidents. During remediation, prioritize high-impact vulnerabilities and make a plan for periodic assessments.

9.        Apply Least-Privilege Access Controls
Employees should have access only to the systems, applications, and data necessary to perform their jobs. By removing unnecessary administrative privileges and regularly reviewing user permissions, organizations can reduce the risk of unauthorized access and limit the damage caused by compromised accounts or insider threats.

Organizations should regularly review their cybersecurity programs, stay informed about emerging threats, and evaluate whether their security controls are adapting to the evolving threat landscape.

Article

Ransomware Trends & Data Insights: August 2026

Although INC Ransom was the most active ransomware threat group observed in August 2026, overall activity remained relatively distributed across the threat landscape, with approximately 30 unique ransomware and extortion groups, including both established and newly emerged actors, observed during the month. Alongside INC Ransom, Storm, DragonForce, Qilin, and The Gentlemen were among the top five most active threat groups, underscoring the continued diversity and competitiveness of the ransomware ecosystem.

Figure 1. Activity from the top 5 threat groups in August 2026 

Throughout the month, analysts at Arete identified several trends behind the threat actors perpetrating cybercrime activities: 

  • August 2026 was a significant month for the emergence of new threat actors, with Arete identifying several previously untracked ransomware groups, including Storm, Dark Project, DireWolf, Morpheus, Orova, Spirals, Wallstreet Team, and Zawoo Team. This trend underscores the ongoing evolution of the threat landscape and the low barriers to entry for new cybercriminal operations. 

  • Arete observed a continued use of AI-enabled capabilities across the ransomware ecosystem during August. Similar to Fulcrumsec and Setra, the newly emerged Zawoo Team appears to leverage AI-assisted analysis of stolen data, publishing highly structured and detailed victim assessments on its data leak site (DLS). Additionally, both the Aurora and Storm ransomware groups have been observed utilizing AI-driven or AI-assisted chat representatives during victim communications. These developments suggest that threat actors are increasingly incorporating AI to enhance data analysis, automate victim engagement, and improve the effectiveness and scalability of extortion operations. 

  • In August, Arete also observed that despite sustained law enforcement disruption efforts, LockBit continues to demonstrate operational resilience, likely through the expansion of its affiliate ecosystem and the evolution of its tactics. Recent activity indicates an increased reliance on data theft and exfiltration-only operations, with observed victimology suggesting a notable focus on organizations within the financial sector. 

Sources

  • Arete Internal

Report

Arete's H1 2026 Crimeware Report

Explore intelligence and operational data from the frontlines of ransomware and extortion response as we detail the most significant trends and developments in the cyber threat landscape in the first half (H1) of 2026.