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Deceived by the Algorithm
The Silent Risk Behind the Quest for Government Efficiency
In 2019, an employee received a call from his company’s CEO, instructing him to transfer $243,000 to a supplier. The employee complied — except it wasn’t the CEO at all, but a synthetic AI voice engineered to deceive.
Although this wasn’t a government agency that was attacked, it still highlights AI’s power to mislead.
When DOGE, the so-called Department of Government Efficiency, began influencing the Trump administration’s agenda to streamline the government and root out “waste and fraud,” it seemed like a good idea.
There’s little dispute that the federal government’s spending habits could use an overhaul, which is why such an initiative makes perfect sense—at least in theory.
Anomalies in Implementation
Initially, many of the cuts aligned with the administration’s stated goals, such as dismantling the Department of Education, but as the process unfolded, a peculiar pattern began to emerge. There was an incident where DOGE leadership reported that 150-year-old people were still receiving Social Security benefits, and then there were recommendations to shutter other government agencies and cut certain programs.
These actions alone were not originally a cause for immediate alarm, except by virtue of the fact that they were causing political fallout; however, when DOGE began culling government workers, alarm bells began going off. The personnel cuts weren’t the issue; it was the seemingly haphazard way they were applied. Workers were sent generic emails that essentially said, “We are contacting you with an offer to sever your employment voluntarily. If you accept this offer, you will be given a severance package, but if you refuse, you risk involuntary dismissal.”
AI’s Role in Personnel Decisions?
Reportedly, nearly every government employee received this message, which alone was strikingly odd. Businesses don’t typically send out company-wide boilerplate messages like this, but things got even stranger when the actual personnel cuts came.
As thousands of government employees began getting pink slips, the peculiar pattern worsened. Reports began to emerge about people not receiving proper notice of termination, while others were shocked to find out they were being let go because of “performance” issues, despite consistently receiving excellent performance evaluations.
At first, much of the turmoil was blamed on incompetence and a lack of understanding of how government functions, but closer examination of the situation reveals the possibility of something much darker, with ramifications that could extend far beyond government staffing issues and politically charged agency and program closures.
A closer look at the chain of events suggests these decisions might not be as haphazard as initially thought, but deliberate, not based on meticulous review but on an algorithm.
As DOGE continued its wrecking ball approach to streamlining the government, reports of AI influence on the process began to surface.
This raises an important and still unanswered question: What AI is DOGE using?
Elon Musk, the de facto leader of DOGE, heads xAI, the company that developed Grok—an advanced generative AI integrated into X, the social media platform (formerly known as Twitter), which he also controls.
Although there is no public confirmation that Grok is the AI being used by DOGE, the overlap is hard to ignore: DOGE reportedly uses AI to make recommendations on personnel and policies. Musk controls Grok and DOGE.
If Grok isn’t the purported AI behind the decision-making, then what system is? After all, if a tool like Grok is available to DOGE, why wouldn’t it be used?
If these reports are accurate, DOGE’s use of AI isn’t just problematic, it’s downright concerning. Here’s why:
Personnel modifications.
Many companies are beginning to use AI to assist with personnel decisions. However, in most cases, a human reviews the data before any actions are taken. With DOGE, there appears to be little, if any, human oversight. This became evident concerning the cases of firings ostensibly related to performance when performance was verifiably not an issue according to actual employee records.
The seeming conflict between the reason for termination and actual performance records is a glaring inconsistency, but let’s explore a little deeper into what a “poor performer” might look like to an AI.
Suppose a person’s medical history indicates a high probability of them developing a condition, or they have a disability that could cause excessive time off from work. An AI might flag this individual for termination despite stellar performance reviews if it has been trained to look for such things. This isn’t a far-fetched notion because companies can access certain employee health information. This includes information for ADA (Americans with Disabilities Act) work accommodations.
Additionally, if an untrained AI were simply instructed to reduce employment by x%, it might generate an email like the ones government workers purportedly received because it wouldn’t have the information to distinguish between who needs to stay and who needs to go.
Dismantling agencies and programs.
Although this area can be influenced strictly by politics, AI fingerprints also turn up here. While the intent to close the Department of Education was publicly announced by the Trump administration, other agencies were also caught up in DOGE’s dragnet, like USAID. Again, the decision to shut down USAID could have been purely partisan, but other agencies slated for the chopping block seemed less likely. The National Oceanic and Atmospheric Administration (NOAA) also appeared in DOGE’s crosshairs.
Why is this significant? This is precisely the type of agency an un-nuanced AI would target for dissolution. We humans realize the importance of predicting the weather and analyzing its patterns. To an untrained or un-nuanced AI, NOAA would look like dead wood because an AI doesn’t care about the weather unless instructed to do so.
Signs of disloyalty or dissent.
There is reporting that DOGE allegedly used AI to scan employees' messages, emails, social media posts, etc, for indications of anyone critical of Trump or his administration. Grok most certainly has the ability to scan every single account on the X platform for criticisms of Trump, and it’s not a stretch to assume it can also cross-reference such posts with government employees or even prospective ones.
The Trump administration, in concert with the Heritage Foundation’s Project 2025 agenda, has expressed a desire to staff the government with those loyal to President Trump. An AI like Grok, directly connected to one of the largest social media platforms in the world, would be the perfect engine to accomplish this, not just with precision but with speed that no human reviewer can match.
Even software designed to look for specific keywords cannot accomplish this task as efficiently as Grok likely can.
So far, if proven to be accurate, these things are extremely unsettling because they signal a disturbing alliance between authoritarian government and algorithmic automation.
However, there is another aspect of these developments, a possibility that is perhaps the most chilling—the integration or access of government systems by an unvetted AI.
If this turns out to be the case, it could be the most significant security breach this country has ever experienced.
From a cybersecurity perspective, granting network access to an unvetted AI would represent a critical security vulnerability, but from a national security perspective, such a breach would be devastating. Here’s why:
An unvetted AI can wreak absolute havoc with a system, especially if it’s given root access to it. Root access is a condition that grants access to a system’s entire infrastructure. In other words, you’ve given it a master key. Obviously, this could be a bad thing because the AI could modify system configurations, override safety directives, and even install malware.
Introducing an unvetted AI into a system can also produce equally problematic unintended consequences like mistakenly overwriting vital information or misinterpreting data, as is suspected to have been the case when DOGE reported 150-year-old Social Security recipients.
This is an obvious case of misinterpreted data because the Social Security database automatically flags all accounts in which the recipient reaches the age of 115 to cease benefit distribution. This policy has been in place since 2015, long before DOGE existed. What was likely observed and misinterpreted was the way the machine processed the data, which was an artifact of the programming language used to build the database.
Now that we’ve addressed standard cybersecurity ramifications, let’s look at the potential national security consequences of unvetted AI access to government systems.
We’ve already noted some obvious security issues previously, but there are even more to consider. Government systems are vast networks of interconnected systems that span the entire country. Although specifics of these networks are rightfully classified, basic network architecture still applies. Without going too far into the technical weeds, here are some things that a properly trained and configured AI can do inside a system:
Network mapping.
While network mapping can be done externally, certain systems may not be visible from the outside—even to a scan by advanced AI. This is due to firewalling, intrusion detectors, DMZs, etc. An AI with internal network access can “see” these systems depending on its level of access. However, a rogue AI might be able to get around systems that detect unauthorized access, allowing it to map the entire network.
Privilege Escalation.
Once an AI system has mapped a network, its next step could be to move laterally, probing for weak or misconfigured permissions, dormant accounts, or forgotten admin credentials. In a government environment, the AI could escalate privileges, granting itself deeper access, or even take over critical administrative controls without raising immediate red flags.
This is a very important point because even if the AI just has “read-only” privileges, it can still glean enough information to gain deeper access into the system.
This can happen in several ways. Reconnaissance is a preliminary step in a system breach. It’s an attack vector. It is used to size up the attack surface of the target system. Depending on the system settings, an adversarial AI might be able to see user lists, client lists, and even configuration and privilege files. Even if the AI isn’t initially granted read-only access to this type of data, a sophisticated AI with internal network access can quickly probe for backdoors, misconfigurations, or weak spots in code and protocols to escalate its reach.
Lateral Movement & Mapping.
Once the AI gains further access to a system, it can begin using techniques such as RDP/SMB enumeration, Pass-the-hash, and Kerberos ticket abuse.
Evasion.
Just as in real-life covert operations, evasion, the ability to escape detection, is vital to the success of a system breach. The attacker must get in and out undetected. This is where AI excels.
It can mask scanning as regular traffic during business hours.
Limit its own bandwidth and packet volume to prevent triggering alerts.
Utilize “Living off the land” (LOTL) techniques that take advantage of unsecured system utilities.
A sophisticated AI can manipulate log entries to hide its fingerprints. A rogue AI can do what the most skilled hackers can — only with more precision and persistence. It is even possible for it to “poison” or bypass internal monitoring systems.
Compromise & Silent Spread.
Once the AI is deeply embedded into a network, that’s when the real horrors begin. At this point, the rogue AI has become nearly unremovable and indistinguishable from normal network processes. The chilling fact is that even the most seasoned human defenders won’t recognize what’s happening until it’s too late.
What we’ve outlined here isn’t just some hypothetical sci-fi scenario. The technology exists right now. This is a legitimate description of a potential attack, grounded in science and backed by experimentation and real-world events. While there are no widely acknowledged, publicly documented cases of a fully autonomous AI breaching a network, there are certainly examples of semi-autonomous or AI-driven attacks.
Systems like WormGPT and FraudGPT are semi-autonomous AI-driven applications that produce nearly undetectable phishing and social engineering attacks. Automated vulnerability tools have been used to conduct attacks, and proof-of-concept AIs have been created that can autonomously scan and infiltrate networks as described above.
So, we see that if government systems were hijacked in this way, the results would be beyond catastrophic.
Perhaps some reading this article might believe that the government has far more hardened defenses against such attacks (and hopefully, it does), but there is one thing we should always bear in mind — No system is completely invulnerable.
Today, vulnerabilities can be uncovered and exploited with frightening speed through AI-powered tools. This is why we must continue to augment defenses capable of meeting this ever-evolving threat.
Also, we should never cede to the algorithm because once you ask it to decide who stays, who goes, and who matters, it won’t ask if you’re human… it will only ask if you’re efficient.
