Joe is the VP of Product Marketing at MixMode. He has led product marketing for multiple cybersecurity companies, with stops at Anomali, FireEye, Neustar and Nextel, as well as various start-ups. Originally from NY, Joe resides outside Washington DC and has a BA from Iona University.
With all eyes on the potential benefits and threats posed by emerging AI-driven products like GPT-4, ChatGPT, AlphaCode, GitHub Copilot, and more, it’s no wonder the federal government is considering how it might harness this technology for homeland security. Department of Homeland Security (DHS) Secretary Alejandro Mayorkas recently announced the creation of a task force focused on the use of AI at a recent Council on Foreign Relations event.
“Our department will lead in the responsible use of AI to secure the homeland,” Mayorkas said at the event on April 21. He emphasized the need for a focus on AI saying, “We must never allow ourselves to be susceptible to ‘failures of imagination,’ which, as the 9/11 Commission concluded nearly 20 years ago, held us back from connecting the dots and preparing for the destruction that was being planned on that tragic day. We must instead look to the future and imagine the otherwise unimaginable, to ensure that whatever threats we face, our Department – our country – will be positioned to meet the moment.”
National Cybersecurity Strategy Urges Federal Agencies to Invest More in Advanced Security
This DHS announcement comes on the heels of the March release of the Biden-Harris Administration’s National Cybersecurity Strategy. As a blueprint on how to best handle issues related to cybercrime and national defense, the strategy specifically points to federal agencies in its guidelines and recommendations, stressing the importance of investing in more advanced security.
With the formation of the new artificial intelligence (AI) Task Force, DHS seems to be in lock-step with the national strategy, answering the call for innovation and investment in advanced cybersecurity to protect critical infrastructure within U.S. borders.
DHS to Focus on 4 Primary Objectives to Advance Critical Homeland Security Missions
1. AI to Protect Supply Chain & Trade Environments
One of the primary applications of AI within the DHS will be to enhance the integrity of supply chains and the broader trade environment. By integrating AI into their operations, the DHS aims to screen cargo more effectively, identify goods produced with forced labor, and manage risk associated with trade.
2. AI to Detect Criminal Drug and Chemical Activity
Another significant area where AI will be employed is in countering the flow of fentanyl into the United States. The DHS plans to leverage AI technology to improve the detection of fentanyl shipments, identify and intercept the flow of precursor chemicals globally, and disrupt key nodes in criminal networks involved in fentanyl trafficking.
3. AI as a Digital Forensic Tool to Intercept and Prevent Underage Digital Exploitation
The AI Task Force will also play a crucial role in combating online child sexual exploitation and abuse. By applying AI to digital forensic tools, the DHS aims to identify, locate, and rescue victims of these heinous crimes, as well as apprehend the perpetrators.
4. AI to Secure Critical Infrastructure
Collaboration with partners in government, industry, and academia will be essential to assess the impact of AI on securing critical infrastructure. The task force will work closely with these stakeholders to evaluate how AI can strengthen the protection of vital systems and facilities.
Additionally, Mayorkas has assigned Homeland Security Advisory Council Co-Chair Jamie Gorelick to study how AI and homeland security intersect. To ensure accountability and progress, the AI Task Force will regularly report to Secretary Mayorkas on its work and AI initiatives across the DHS.
Using AI for Cybersecurity
The use of Artificial Intelligence (AI) in cybersecurity is becoming increasingly important for organizations to prevent cyber-attacks. AI is capable of analyzing enormous amounts of data, hundreds and thousands of times faster than humans can, enabling effective threat detection. AI systems are able to learn from previous experiences and recognize patterns that lead to more accurate predictions with fewer false positives for security teams.
Organizations are able to make better decisions about cybersecurity threats by utilizing AI, helping them detect malicious behavior quickly. For example, traditional security solutions rely on signature-based detection methods which may not be precise enough when dealing with a huge number of computer networks or connections that need constant monitoring. On the other hand, AI-based tools are able to leverage autonomous analysis capabilities, going beyond signature-based schemes, allowing security teams to explore anomalies in network traffic on an ongoing basis while reducing tedious tasks.
The establishment of the AI Task Force by the DHS demonstrates a commitment to harnessing the potential of AI in addressing emerging threats and safeguarding national security. By leveraging AI technology in various areas, such as supply chain integrity, countering drug trafficking, combating online child exploitation, and securing critical infrastructure, the DHS aims to stay ahead of evolving risks and protect the nation more effectively.
How MixMode Leverages Third Wave AI to Meet the Strictest Security Demands
The MixMode platform stands apart from a field of cybersecurity vendors still relying on legacy approaches to network security, including first and second wave AI technology that use rules-based approaches. These outdated platforms are inherently limited to the manually created rules and self-selected training data and take up to two years to become effective. An endless cycle of training the data, updating rules, and manually reviewing mountains of false positive flags ensues.
As the first commercially available platform leveraging true Third Wave AI, (as defined by DARPA) MixMode is different and is the only cybersecurity platform that leverages a dynamical threat detection foundation model to predict known and novel attacks, including the ability to surface zero-day attacks in real time with 90%+ alert precision and reduction.
MixMode employs self-supervised learning to develop a forecast of expected network behavior free from human input in less than 7 days. Unlike first and second wave approaches, MixMode is context-aware — it can independently extract patterns and trends from the underlying time-stamped data. This distinction is critical, adding a sophisticated level of cybersecurity capable of defending against the modern threatscape, including the increasing threat of ransomware.
MixMode recognizes that networks are constantly evolving, as are threats. MixMode uses self-learning AI, wielded expertly and effectively by veterans with decades of experience in the field of AI.
Learn more about the MixMode platform and set up a demo today.
Other MixMode Articles You Might Like
Evolving Role of the CISO: From IT Security to Business Resilience
Channeltivity: Understanding Global Channel Management